Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various doma...Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.展开更多
A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete...A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.展开更多
Anchor-free object-detection methods achieve a significant advancement in field of computer vision,particularly in the realm of real-time inferences.However,in remote sensing object detection,anchor-free methods often...Anchor-free object-detection methods achieve a significant advancement in field of computer vision,particularly in the realm of real-time inferences.However,in remote sensing object detection,anchor-free methods often lack of capability in separating the foreground and background.This paper proposes an anchor-free method named probability-enhanced anchor-free detector(ProEnDet)for remote sensing object detection.First,a weighted bidirectional feature pyramid is used for feature extraction.Second,we introduce probability enhancement to strengthen the classification of the object’s foreground and background.The detector uses the logarithm likelihood as the final score to improve the classification of the foreground and background of the object.ProEnDet is verified using the DIOR and NWPU-VHR-10 datasets.The experiment achieved mean average precisions of 61.4 and 69.0 on the DIOR dataset and NWPU-VHR-10 dataset,respectively.ProEnDet achieves a speed of 32.4 FPS on the DIOR dataset,which satisfies the real-time requirements for remote-sensing object detection.展开更多
In the field of remote sensing,the rapid and accurate acquisition of the category and location of airplanes has emerged as a prominent research.However,remote sensing fuzzy imaging and complex environmental interferen...In the field of remote sensing,the rapid and accurate acquisition of the category and location of airplanes has emerged as a prominent research.However,remote sensing fuzzy imaging and complex environmental interference affect airplane detection.Besides,the inconsistency in the size of remote sensing images and the low accuracy of small target detection are crucial challenges that need to be addressed.To tackle these issues,we propose a novel network SDaDCS(SAHI-data augmentation-dilation-channel and spatial attention)based on YOLOX model and the slicing aided hyper inference(SAHI)framework,a new data augmentation technique and dilation-channel and spatial(DCS)attention mechanism.Initially,we create a remote sensing dataset for airplane targets and introduce a new data augmentation technique based on the Rotate-Mixup and mixed data augmentation to enhance data diversity.The DCS attention mechanism,which comprises the dilated convolution block,channel attention and spatial attention,is designed to bolster the feature extraction and discrimination of the network.To address the challenges arised by the difficulties of detecting small targets,we integrate the YOLOX model with the SAHI framework.Experiment results show that,when compared to the original YOLOX model,the proposed SDaDCS remote sensing target detection algorithm enhances overall accuracy by 13.6%.The experimental results validate the effectiveness of the proposed algorithm.展开更多
This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the second...This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
Maize tassel detection is essential for future agronomic management in maize planting and breeding,with application in yield estimation,growth monitoring,intelligent picking,and disease detection.However,detecting mai...Maize tassel detection is essential for future agronomic management in maize planting and breeding,with application in yield estimation,growth monitoring,intelligent picking,and disease detection.However,detecting maize tassels in the field poses prominent challenges as they are often obscured by widespread occlusions and differ in size and morphological color at different growth stages.This study proposes the SEYOLOX-tiny Model that more accurately and robustly detects maize tassels in the field.Firstly,the data acquisition method ensures the balance between the image quality and image acquisition efficiency and obtains maize tassel images from different periods to enrich the dataset by unmanned aerial vehicle(UAV).Moreover,the robust detection network extends YOLOX by embedding an attention mechanism to realize the extraction of critical features and suppressing the noise caused by adverse factors(e.g.,occlusions and overlaps),which could be more suitable and robust for operation in complex natural environments.Experimental results verify the research hypothesis and show a mean average precision(mAP_(@0.5)) of 95.0%.The mAP_(@0.5),mAP_(@0.5-0.95),mAP_(@0.5-0.95(area=small)),and mAP_(@0.5-0.95(area=medium)) average values increased by 1.5,1.8,5.3,and 1.7%,respectively,compared to the original model.The proposed method can effectively meet the precision and robustness requirements of the vision system in maize tassel detection.展开更多
Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can pro...Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.展开更多
Methods of arc length control and visual image based weld detection for precision pulse TIG welding were investigated. With a particular all hardware circuit, arc voltage during peak current stage is sampled and inte...Methods of arc length control and visual image based weld detection for precision pulse TIG welding were investigated. With a particular all hardware circuit, arc voltage during peak current stage is sampled and integrated to indicate arc length, deviation of arc length and adjusting parameters are calculated and output to drive a step motor directly. According to the features of welding image grabbed with CCD camera, a special algorithm was developed to detect the central line of weld fast and accurately. Then an application system were established, whose static arc length error is ±0.1 mm with 20 A average current and 1 mm given arc length, static detection precision of weld is 0.01 mm , processing time of each image is less than 120 ms . Precision pulse TIG welding of some given thin stainless steel components with complicated curved surface was successfully realized.展开更多
The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resoluti...The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.展开更多
Polythiophene/WO3（PTP/WO3）organic-inorganic hybrids were synthesized by an in situ chemical oxidative polymerization method,and char- acterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM）and ...Polythiophene/WO3（PTP/WO3）organic-inorganic hybrids were synthesized by an in situ chemical oxidative polymerization method,and char- acterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM）and thermo-gravimetric analysis（TGA）.The Polythiophene/ WO3 hybrids have higher thermal stability than pure polythiophene,which is beneficial to potential application as chemical sensors.Gas sensing measurements demonstrate that the gas sensor based on the Polythiophene/WO3 hybrids has high response and good selectivity for de- tecting NO2 of ppm level at low temperature.Both the operating temperature and PTP contents have an influence on the response of PTP/WO3 hybrids to NO2.The 10 wt%PTP/WO3 hybrid showed the highest response at low operating temperature of 70-C.It is expected that the PTP/WO3 hybrids can be potentially used as gas sensor material for detecting the low concentration of NO2 at low temperature.展开更多
There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge ...There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge detection method for remote sensing image based on variable structuring element is proposed. Firstly, the structuring elements with different scales and multiple directions are constructed according to the diversity of remote sensing imagery targets. In order to suppress the noise of the target background and highlight the edge of the image target in the remote sensing image by adaptive Top hat and Bottom hat transform, the corresponding adaptive morphological operations are constructed based on variable structuring elements; Secondly, adaptive morphological edge detection is used to obtain multiple images with different scales and directional edge features; Finally, the image edges are obtained by weighted summation of each direction edge, and then the least square is used to fit the edges for accurate location of the edge contour of the target. The experimental results show that the proposed method not only can detect the complete edge of remote sensing image, but also has high edge detection accuracy and superior anti-noise performance. Compared with classical edge detection and the morphological edge detection with a fixed single structuring element, the proposed method performs better in edge detection effect, and the accuracy of detection can reach 95 %展开更多
Cooperation in spectral sensing （SS） offers a fast and reliable detection of primary user （PU） transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center （FC） ...Cooperation in spectral sensing （SS） offers a fast and reliable detection of primary user （PU） transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center （FC） has to handle a large set of data, a duster based approach, specifically fuzzy c-means clustering （FCM）, has been extensively used in energy detection based cooperative spectrum sensing （CSS）. However, the performance of FCM degrades at low signal-to-noise ratios （SNR） and in the presence of multiple PUs as energy data patterns at the FC are often found to be non-spherical i.e. overlapping. To address the problem, this work explores the scope of kernel fuzzy c-means （KFCM） on energy detection based CSS through the projection of non-linear input data to a high dimensional feature space. Extensive simulation results are shown to highlight the improved detection of multiple PUs at low SNR with low energy consumption. An improvement in the detection probability by ～6.78% and ～6.96% at -15 dBW and -20 dBW, respectively, is achieved over the existing FCM method.展开更多
The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great...The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.展开更多
Remote sensing techniques are proven methods for quantifying chlorophyll-a levels by inference algal concentrations in reservoirs. One traditional method is to use Landsat imagery and field data from a limited time pe...Remote sensing techniques are proven methods for quantifying chlorophyll-a levels by inference algal concentrations in reservoirs. One traditional method is to use Landsat imagery and field data from a limited time period to develop a model for a reservoir which relates reflectance in various bands to measured algal (or chlorophyll-a) concentrations and use that model and associated imagery to determine spatial algal concentrations in the reservoir. In this work, we extend these techniques to use historical Landsat data over long time periods to develop seasonal models that will more accurately describe the conditions throughout the growing season. Previous work at Deer Creek included the development of a chlorophyll-a model using data from the months of August to September. This model did not account for seasonal variation and algal succession, which affects the relationship between measured reflectance and algal concentration. Early summer algal blooms are dominated by diatoms (yellow-brown), while the algae vary from chlorophyta (green) in the mid-summer to cyanobacteria (blue-green) in late summer months. This study presents and explores the development and use of seasonal algorithms based on reflective characteristics of various algal communities to create a more accurate model for the reservoir. This study uses water quality data collected over a 20-year period during non-ice conditions along with associated Landsat data. As the field measurements were not taken to support remote sensing measurements, this study evaluates the use of historical data to support remote sensing analysis. It is assumed that reservoir conditions do not change rapidly, the field data can be used to develop correlations with satellite imagery taken within a day of the field measurements, and the seasonal algal communities have different reflective properties (or colors). We present statistical analysis that shows the seasonal algorithms better fit the data than the non-seasonal model and the traditional model calibrated with late-season data. We recommend the use of sub-seasonal algorithms to more accurately model and predict water quality throughout the growing season.展开更多
We propose a ground moving target detection method for dual-channel Wide Area Surveillance(WAS) radar based on Compressed Sensing(CS).Firstly,the method of moving target detection of the WAS radar is studied.In order ...We propose a ground moving target detection method for dual-channel Wide Area Surveillance(WAS) radar based on Compressed Sensing(CS).Firstly,the method of moving target detection of the WAS radar is studied.In order to reduce the sample data quantity of the radar,the echo data is randomly sampled in the azimuth direction,then,the matched filter is used to perform the range direction focus.We can use the compressive sensing theory to recover the signal in the Doppler domain.At last,the phase difference between the two channels is compensated to suppress the clutter.The result of the simulated data verifies the effectiveness of the proposed method.展开更多
Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering the...Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.展开更多
This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based regi...This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.展开更多
The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has ...The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.展开更多
In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usual...In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usually havelarge size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detectobjects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reducethe runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC.The formal representation and details of two these procedures are presented in thispaper. The experiments on satellite image and remote sensing image datasets aregiven to evaluate proposed model. Validity indices and time consuming are usedto compare proposed model to picture fuzzy clustering model. The values ofvalidity indices show that picture fuzzy clustering integrated to MapReduce getsbetter quality of segmentation than using picture fuzzy clustering only. Moreover,on two selected image datasets, the run time of MPFC model is much less thanthat of picture fuzzy clustering.展开更多
文摘Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.
基金supported by the Key Area R&D Program of Guangdong Province (Grant No.2022B0701180001)the National Natural Science Foundation of China (Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China (Grant Nos.2019B010140002 and 2020B111110002)the Guangdong-Hong Kong-Macao Joint Innovation Field Project (Grant No.2021A0505080006)。
文摘A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.
基金supported in part by the National Natural Science Foundation of China(42001408).
文摘Anchor-free object-detection methods achieve a significant advancement in field of computer vision,particularly in the realm of real-time inferences.However,in remote sensing object detection,anchor-free methods often lack of capability in separating the foreground and background.This paper proposes an anchor-free method named probability-enhanced anchor-free detector(ProEnDet)for remote sensing object detection.First,a weighted bidirectional feature pyramid is used for feature extraction.Second,we introduce probability enhancement to strengthen the classification of the object’s foreground and background.The detector uses the logarithm likelihood as the final score to improve the classification of the foreground and background of the object.ProEnDet is verified using the DIOR and NWPU-VHR-10 datasets.The experiment achieved mean average precisions of 61.4 and 69.0 on the DIOR dataset and NWPU-VHR-10 dataset,respectively.ProEnDet achieves a speed of 32.4 FPS on the DIOR dataset,which satisfies the real-time requirements for remote-sensing object detection.
基金supported in part by National Natural Science Foundation of China(No.62471034)Hebei Natural Science Foundation(No.F2023105001)。
文摘In the field of remote sensing,the rapid and accurate acquisition of the category and location of airplanes has emerged as a prominent research.However,remote sensing fuzzy imaging and complex environmental interference affect airplane detection.Besides,the inconsistency in the size of remote sensing images and the low accuracy of small target detection are crucial challenges that need to be addressed.To tackle these issues,we propose a novel network SDaDCS(SAHI-data augmentation-dilation-channel and spatial attention)based on YOLOX model and the slicing aided hyper inference(SAHI)framework,a new data augmentation technique and dilation-channel and spatial(DCS)attention mechanism.Initially,we create a remote sensing dataset for airplane targets and introduce a new data augmentation technique based on the Rotate-Mixup and mixed data augmentation to enhance data diversity.The DCS attention mechanism,which comprises the dilated convolution block,channel attention and spatial attention,is designed to bolster the feature extraction and discrimination of the network.To address the challenges arised by the difficulties of detecting small targets,we integrate the YOLOX model with the SAHI framework.Experiment results show that,when compared to the original YOLOX model,the proposed SDaDCS remote sensing target detection algorithm enhances overall accuracy by 13.6%.The experimental results validate the effectiveness of the proposed algorithm.
文摘This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the Chinese Universities Scientific Fund (2022TC169)。
文摘Maize tassel detection is essential for future agronomic management in maize planting and breeding,with application in yield estimation,growth monitoring,intelligent picking,and disease detection.However,detecting maize tassels in the field poses prominent challenges as they are often obscured by widespread occlusions and differ in size and morphological color at different growth stages.This study proposes the SEYOLOX-tiny Model that more accurately and robustly detects maize tassels in the field.Firstly,the data acquisition method ensures the balance between the image quality and image acquisition efficiency and obtains maize tassel images from different periods to enrich the dataset by unmanned aerial vehicle(UAV).Moreover,the robust detection network extends YOLOX by embedding an attention mechanism to realize the extraction of critical features and suppressing the noise caused by adverse factors(e.g.,occlusions and overlaps),which could be more suitable and robust for operation in complex natural environments.Experimental results verify the research hypothesis and show a mean average precision(mAP_(@0.5)) of 95.0%.The mAP_(@0.5),mAP_(@0.5-0.95),mAP_(@0.5-0.95(area=small)),and mAP_(@0.5-0.95(area=medium)) average values increased by 1.5,1.8,5.3,and 1.7%,respectively,compared to the original model.The proposed method can effectively meet the precision and robustness requirements of the vision system in maize tassel detection.
基金supported by the Foundation of Graduate Innovation Center in NUAA under Grant No. kfjj20190414the open research fund of Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Industry and Information Technology, Nanjing, 211106, China (No. KF20181913)+2 种基金National Natural Science Foundation of China (No. 61631020, No. 61871398, No. 61931011 and No. 61801216)the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20190030)the Natural Science Foundation of Jiangsu Province (No. BK20180420)
文摘Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.
文摘Methods of arc length control and visual image based weld detection for precision pulse TIG welding were investigated. With a particular all hardware circuit, arc voltage during peak current stage is sampled and integrated to indicate arc length, deviation of arc length and adjusting parameters are calculated and output to drive a step motor directly. According to the features of welding image grabbed with CCD camera, a special algorithm was developed to detect the central line of weld fast and accurately. Then an application system were established, whose static arc length error is ±0.1 mm with 20 A average current and 1 mm given arc length, static detection precision of weld is 0.01 mm , processing time of each image is less than 120 ms . Precision pulse TIG welding of some given thin stainless steel components with complicated curved surface was successfully realized.
基金National Natural Science Foundation of China(No.41871305)National Key Research and Development Program of China(No.2017YFC0602204)+2 种基金Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(No.CUGQY1945)Open Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education and the Fundamental Research Funds for the Central Universities(No.GLAB2019ZR02)Open Fund of Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources,China(No.KF-2020-05-068)。
文摘The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.
基金financially supported by the National Natural Science Foundation of China(No.20871071)the Science and Technology Commission Foundation of Tianjin(No.09JCYBJC03600 and 10JCYBJC03900)
文摘Polythiophene/WO3（PTP/WO3）organic-inorganic hybrids were synthesized by an in situ chemical oxidative polymerization method,and char- acterized by X-ray diffraction（XRD）,transmission electron microscopy（TEM）and thermo-gravimetric analysis（TGA）.The Polythiophene/ WO3 hybrids have higher thermal stability than pure polythiophene,which is beneficial to potential application as chemical sensors.Gas sensing measurements demonstrate that the gas sensor based on the Polythiophene/WO3 hybrids has high response and good selectivity for de- tecting NO2 of ppm level at low temperature.Both the operating temperature and PTP contents have an influence on the response of PTP/WO3 hybrids to NO2.The 10 wt%PTP/WO3 hybrid showed the highest response at low operating temperature of 70-C.It is expected that the PTP/WO3 hybrids can be potentially used as gas sensor material for detecting the low concentration of NO2 at low temperature.
基金National Natural Science Foundation of China(No.61761027)Postgraduate Education Reform Project of Lanzhou Jiaotong University(No.1600120101)
文摘There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge detection method for remote sensing image based on variable structuring element is proposed. Firstly, the structuring elements with different scales and multiple directions are constructed according to the diversity of remote sensing imagery targets. In order to suppress the noise of the target background and highlight the edge of the image target in the remote sensing image by adaptive Top hat and Bottom hat transform, the corresponding adaptive morphological operations are constructed based on variable structuring elements; Secondly, adaptive morphological edge detection is used to obtain multiple images with different scales and directional edge features; Finally, the image edges are obtained by weighted summation of each direction edge, and then the least square is used to fit the edges for accurate location of the edge contour of the target. The experimental results show that the proposed method not only can detect the complete edge of remote sensing image, but also has high edge detection accuracy and superior anti-noise performance. Compared with classical edge detection and the morphological edge detection with a fixed single structuring element, the proposed method performs better in edge detection effect, and the accuracy of detection can reach 95 %
文摘Cooperation in spectral sensing （SS） offers a fast and reliable detection of primary user （PU） transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center （FC） has to handle a large set of data, a duster based approach, specifically fuzzy c-means clustering （FCM）, has been extensively used in energy detection based cooperative spectrum sensing （CSS）. However, the performance of FCM degrades at low signal-to-noise ratios （SNR） and in the presence of multiple PUs as energy data patterns at the FC are often found to be non-spherical i.e. overlapping. To address the problem, this work explores the scope of kernel fuzzy c-means （KFCM） on energy detection based CSS through the projection of non-linear input data to a high dimensional feature space. Extensive simulation results are shown to highlight the improved detection of multiple PUs at low SNR with low energy consumption. An improvement in the detection probability by ～6.78% and ～6.96% at -15 dBW and -20 dBW, respectively, is achieved over the existing FCM method.
基金This work was supported by the National Advance Research Program(Item No.Y1601-1).
文摘The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.
文摘Remote sensing techniques are proven methods for quantifying chlorophyll-a levels by inference algal concentrations in reservoirs. One traditional method is to use Landsat imagery and field data from a limited time period to develop a model for a reservoir which relates reflectance in various bands to measured algal (or chlorophyll-a) concentrations and use that model and associated imagery to determine spatial algal concentrations in the reservoir. In this work, we extend these techniques to use historical Landsat data over long time periods to develop seasonal models that will more accurately describe the conditions throughout the growing season. Previous work at Deer Creek included the development of a chlorophyll-a model using data from the months of August to September. This model did not account for seasonal variation and algal succession, which affects the relationship between measured reflectance and algal concentration. Early summer algal blooms are dominated by diatoms (yellow-brown), while the algae vary from chlorophyta (green) in the mid-summer to cyanobacteria (blue-green) in late summer months. This study presents and explores the development and use of seasonal algorithms based on reflective characteristics of various algal communities to create a more accurate model for the reservoir. This study uses water quality data collected over a 20-year period during non-ice conditions along with associated Landsat data. As the field measurements were not taken to support remote sensing measurements, this study evaluates the use of historical data to support remote sensing analysis. It is assumed that reservoir conditions do not change rapidly, the field data can be used to develop correlations with satellite imagery taken within a day of the field measurements, and the seasonal algal communities have different reflective properties (or colors). We present statistical analysis that shows the seasonal algorithms better fit the data than the non-seasonal model and the traditional model calibrated with late-season data. We recommend the use of sub-seasonal algorithms to more accurately model and predict water quality throughout the growing season.
文摘We propose a ground moving target detection method for dual-channel Wide Area Surveillance(WAS) radar based on Compressed Sensing(CS).Firstly,the method of moving target detection of the WAS radar is studied.In order to reduce the sample data quantity of the radar,the echo data is randomly sampled in the azimuth direction,then,the matched filter is used to perform the range direction focus.We can use the compressive sensing theory to recover the signal in the Doppler domain.At last,the phase difference between the two channels is compensated to suppress the clutter.The result of the simulated data verifies the effectiveness of the proposed method.
文摘Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.
文摘This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.
文摘The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.
基金funded by Thuyloi University Foundation for Science and Technologyunder Grant Number TLU.STF.19-02.
文摘In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usually havelarge size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detectobjects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reducethe runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC.The formal representation and details of two these procedures are presented in thispaper. The experiments on satellite image and remote sensing image datasets aregiven to evaluate proposed model. Validity indices and time consuming are usedto compare proposed model to picture fuzzy clustering model. The values ofvalidity indices show that picture fuzzy clustering integrated to MapReduce getsbetter quality of segmentation than using picture fuzzy clustering only. Moreover,on two selected image datasets, the run time of MPFC model is much less thanthat of picture fuzzy clustering.
