China has experienced unprecedented urbanization in the past decades,resulting in dramatic changes in the physical,limnological,and hydrological characteristics of lakes in urban landscapes.However,the spatiotemporal ...China has experienced unprecedented urbanization in the past decades,resulting in dramatic changes in the physical,limnological,and hydrological characteristics of lakes in urban landscapes.However,the spatiotemporal dynamics in distribution and abundance of urban lakes in China remain poorly understood.Here,we characterized the spatiotemporal change patterns of urban lakes in China’s major cities between 1990 and 2015 using remote-sensing data and landscape metrics.The results showed that the urban lake landscape patterns have experienced drastic changes over the past 25 years.The total surface area of the urban lakes has decreased by 17,620.02 ha,a decrease of 24.22%,with a significant increase in the landscape fragmentation and a reduction in shape complexity.We defined three lake-shrinkage types and found that vanishment was the most common lake-shrinkage pattern,followed by edge-shrinkage and tunneling in terms of lake area.Moreover,we also found that urban sprawl was the dominant driver of the lake shrinkage,accounting for 67.89%of the total area loss,and the transition from lakes to cropland was also an important factor(19.86%).This study has potential for providing critical baseline information for government decision-making in lake resources management and urban landscape design.展开更多
An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also pla...An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also plays a part in understanding where,when,and why a particular vehicle interaction has occurred in a certain way.Therefore,it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment,particularly at the intersections.This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents.Moreover,detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure.Conversely,traffic at intersections has been explored mainly for flow estimation,capacity and width measurements,and traffic congestion,etc.,whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored.In this paper,we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence(PDP)representation.We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections.This permits tracking the micro-movement of vehicles in much finer detail,which is used later to retrieve movement patterns from a motion dataset.Unlike conventional approaches,we start our approach with the actual movements before modeling the static intersection environment.Additionally,with the aid of illustrative examples,we discuss how the length,width,and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately.Additionally,we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes.展开更多
Close-range hyperspectral images are a promising source of information in plant biology,in particular,for in vivo study of physiological changes.In this study,we investigate how data fusion can improve the detection o...Close-range hyperspectral images are a promising source of information in plant biology,in particular,for in vivo study of physiological changes.In this study,we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information.The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation,disease infections,and environmental conditions have in plants.We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing.Experimental results demonstrate the efficiency of our fusion approach,with significant improvements over some conventional methods.展开更多
Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers.However,the increasing spectral dimensions,as well as the information redundancy,make the ana...Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers.However,the increasing spectral dimensions,as well as the information redundancy,make the analysis and interpretation of hyperspectral images a challenge.Feature extraction is a very important step for hyperspectral image processing.Feature extraction methods aim at reducing the dimension of data,while preserving as much information as possible.Particularly,nonlinear feature extraction methods (e.g.kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing,due to their good preservation of high-order structures of the original data.However,conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction,and this leads to poor performances for post-applications.This paper proposes a novel nonlinear feature extraction method for hyperspectral images.Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window),the proposed method explores the use of image segmentation.The approach benefits both noise fraction estimation and information preservation,and enables a significant improvement for classification.Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method.Compared to conventional KMNF,the improvements of the method on two hyperspectral image classification are 8 and 11％.This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required.展开更多
基金the National Natural Science Foundation of China(Grants No.41522110 and 41771360)the National Key Research and Development Program of China(Grant No.2016YFB0501403)。
文摘China has experienced unprecedented urbanization in the past decades,resulting in dramatic changes in the physical,limnological,and hydrological characteristics of lakes in urban landscapes.However,the spatiotemporal dynamics in distribution and abundance of urban lakes in China remain poorly understood.Here,we characterized the spatiotemporal change patterns of urban lakes in China’s major cities between 1990 and 2015 using remote-sensing data and landscape metrics.The results showed that the urban lake landscape patterns have experienced drastic changes over the past 25 years.The total surface area of the urban lakes has decreased by 17,620.02 ha,a decrease of 24.22%,with a significant increase in the landscape fragmentation and a reduction in shape complexity.We defined three lake-shrinkage types and found that vanishment was the most common lake-shrinkage pattern,followed by edge-shrinkage and tunneling in terms of lake area.Moreover,we also found that urban sprawl was the dominant driver of the lake shrinkage,accounting for 67.89%of the total area loss,and the transition from lakes to cropland was also an important factor(19.86%).This study has potential for providing critical baseline information for government decision-making in lake resources management and urban landscape design.
基金supported by the Higher Education Commission(HEC),Pakistan[grant number 50040696]Bernard De Baets and Guy De Tréreceived funding from the Flemish Government under the“Onderzoeksprogramma Artificiële Intelligentie(AI)Vlaanderen”program.
文摘An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also plays a part in understanding where,when,and why a particular vehicle interaction has occurred in a certain way.Therefore,it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment,particularly at the intersections.This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents.Moreover,detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure.Conversely,traffic at intersections has been explored mainly for flow estimation,capacity and width measurements,and traffic congestion,etc.,whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored.In this paper,we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence(PDP)representation.We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections.This permits tracking the micro-movement of vehicles in much finer detail,which is used later to retrieve movement patterns from a motion dataset.Unlike conventional approaches,we start our approach with the actual movements before modeling the static intersection environment.Additionally,with the aid of illustrative examples,we discuss how the length,width,and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately.Additionally,we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes.
文摘Close-range hyperspectral images are a promising source of information in plant biology,in particular,for in vivo study of physiological changes.In this study,we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information.The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation,disease infections,and environmental conditions have in plants.We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing.Experimental results demonstrate the efficiency of our fusion approach,with significant improvements over some conventional methods.
基金the National Natural Science Foundation of China [Grant Number 41722108],(Grant Number 91638201)%FWO project:data fusion for image analysis in remote sensing(Grant Number G037115N)
文摘Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers.However,the increasing spectral dimensions,as well as the information redundancy,make the analysis and interpretation of hyperspectral images a challenge.Feature extraction is a very important step for hyperspectral image processing.Feature extraction methods aim at reducing the dimension of data,while preserving as much information as possible.Particularly,nonlinear feature extraction methods (e.g.kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing,due to their good preservation of high-order structures of the original data.However,conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction,and this leads to poor performances for post-applications.This paper proposes a novel nonlinear feature extraction method for hyperspectral images.Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window),the proposed method explores the use of image segmentation.The approach benefits both noise fraction estimation and information preservation,and enables a significant improvement for classification.Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method.Compared to conventional KMNF,the improvements of the method on two hyperspectral image classification are 8 and 11％.This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required.
