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Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images

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Journal of Systems Engineering and Electronics 2022年第4期33卷 896-906页

作者： FENG Hao WU Jianzhong ZHANG Lu LIAO Mingsheng State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan 430079China Key Laboratory of Land Subsidence Monitoring and Prevention Ministry of Land and ResourcesShanghai 200072China Shanghai Engineering Research Center of Land Subsidence Shanghai 200072China Shanghai Institute of Geological Survey Shanghai 200072China

Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban *** existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information,there are still two problems to be solved in practical ***,change indicators constructed from incoherent feature only cannot characterize the change objects ***,the results of pixel-level methods are usually presented in the form of the noisy binary map,making the spatial change not intuitive and the temporal change of a single pixel *** this study,we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR *** coefficients of variation in timeseries incoherent features and the man-made object index(MOI)defined with coherent features are first combined to identify the initial change ***,an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise(DBSCAN)and dynamic time warping(DTW),which can transform the initial results into noiseless object-level patches,and take the cluster center as a representative of the man-made object to determine the change pattern of each *** experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.

关键词： change detection multi-temporal synthetic aperture radar(SAR)data coherent and incoherent features clustering

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Exploring Image Generation for UAV change detection

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1061-1072页

作者： Xuan Li Haibin Duan Yonglin Tian Fei-Yue Wang Peng Cheng Laboratory Shenzhen 518000China IEEE State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083 Department of Automation University of Science and Technology of ChinaHefei 230027 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense TechnologyChangsha 410073China

change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and ***,even the most advanced models require large amounts of data for model training and ***,sufficient labeled images with different imaging conditions are *** by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated *** simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection ***,we propose an image translation framework that translates simulated images to synthetic *** framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training *** experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.

关键词： change detection computer graphics image translation simulated images synthetic images unmanned aerial vehicles(UAVs)

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D2ANet:Difference-aware attention network for multi-level change detection from satellite imagery

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Computational Visual Media 2023年第3期9卷 563-579页

作者： Jie Mei Yi-Bo Zheng Ming-Ming Cheng TMCC CSNankai UniversityTianjin 300350China State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China

Recognizing dynamic variations on the ground,especially changes caused by various natural disasters,is critical for assessing the severity of the damage and directing the disaster ***,current workflows for disaster assessment usually require human analysts to observe and identify damaged buildings,which is labor-intensive and unsuitable for large-scale disaster *** this paper,we propose a difference-aware attention network(D2ANet)for simultaneous building localization and multi-level change detection from the dual-temporal satellite *** the differences in different channels in the features of pre-and post-disaster images,we develop a dual-temporal aggregation module using paired features to excite change-sensitive channels of the features and learn the global change *** the nature of building damage caused by disasters is diverse in complex environments,we design a difference-attention module to exploit local correlations among the multi-level changes,which improves the ability to identify damage on different *** experiments on the large-scale building damage assessment dataset xBD demonstrate that our approach provides new state-of-the-art *** code is publicly available at https://***/mj129/D2ANet.

关键词： change detection building localization sate-llite imagery dual-temporal aggregation difference attention

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Deep learning for change detection in remote sensing:a review

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Geo-Spatial Information Science 2023年第3期26卷 262-288页

作者： Ting Bai Le Wang Dameng Yin Kaimin Sun Yepei Chen Wenzhuo Li Deren Li Project Planning Department Wuhan Land Arranging Storage CenterWuhanChina Department of Geography University at BuffaloThe State University of New YorkBuffaloUSA Institute of Crop Sciences Chinese Academy of Agricultural SciencesBeijingChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Computer Science Hubei University of TechnologyWuhanChina Collaborative Innovation Center of Geospatial Technology Wuhan UniversityWuhanChina

A large number of publications have incorporated deep learning in the process of remote sensing change *** these Deep Learning change detection(DLCD)publications,deep learning methods have demonstrated their superiority over conventional change detection ***,the theoretical underpinnings of why deep learning improves the performance of change detection remain *** of today,few in-depth reviews have investigated the mechanisms of *** such a review,five critical questions remain *** DLCD provide improved information representation for change detection?If so,how?How to select an appropriate DLCD method and why?How much does each type of change benefits from DLCD in terms of its performance?What are the major limitations of existing DLCD methods and what are the prospects for DLCD?To address these five questions,we reviewed according to the following *** grouped the DLCD information assemblages into the four unique dimensions of remote sensing:spectral,spatial,temporal,and *** the extraction of information in each dimension,the difference between DLCD and conventional change detection methods was *** proposed a taxonomy of existing DLCD methods by dividing them into two distinctive pools:separate and coupled *** advantages,limitations,applicability,and performance were thoroughly investigated and explicitly *** examined the variations in performance between DLCD and conventional change *** depicted two limitations of DLCD,*** sample and hardware and software *** on these analyses,we identified directions for future *** a result of our review,we found that DLCD’s advantages over conventional change detection can be attributed to three factors:improved information representation;improved change detection methods;and performance *** has to surpass the limitations with regard to training samples and computing infrastructure.

关键词： Deep learning change detection remote sensing review information representation

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Spectral‐spatial sequence characteristics‐based convolutional transformer for hyperspectral change detection

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1237-1257页

作者： Chengle Zhou Qian Shi Da He Bing Tu Haoyang Li Antonio Plaza School of Geography and Planning Sun Yat‐sen UniversityGuangzhouChina Guangdong Provincial Key Laboratory for Urbanization and GeoSimulation Sun Yat‐sen UniversityGuangzhouChina Institute of Optics and Electronics Nanjing University of Information Science and TechnologyNanjingChina Hyperspectral Computing Laboratory Escuela PolitecnicaUniversity of ExtremaduraCaceresSpain

Recently,ground coverings change detection(CD)driven by bitemporal hyperspectral images(HSIs)has become a hot topic in the remote sensing *** are two challenges in the HSI‐CD task:(1)attribute feature representation of pixel pairs and(2)feature extraction of attribute patterns of pixel *** solve the above problems,a novel spectral‐spatial sequence characteristics‐based convolutional transformer(S3C‐CT)method is proposed for the HSI‐CD *** the designed method,firstly,an eigenvalue extrema‐based band selection strategy is introduced to pick up spectral information with salient attribute ***,a 3D tensor with spectral‐spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal ***,a fusion framework of the convolutional neural network(CNN)and Transformer encoder(TE)is designed to extract high‐order sequence semantic features,taking into account both local context information and global sequence ***,a spatial‐spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and ***,the binary change map is determined according to the fully‐connected *** results on real HSI datasets indicated that the proposed S3C‐CT method outperforms other well‐known and state‐of‐the‐art detection approaches in terms of detection performance.

关键词： change detection imageanalysis

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GCD‑L: A Novel Method for Geometric change detection in HD Maps Using Low‑Cost Sensors

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Automotive Innovation 2022年第3期5卷 324-332页

作者： Peng Sun Yunpeng Wang Peng He Xinxin Pei Mengmeng Yang Kun Jiang Diange Yang Autonomous Driving Technology Baidu IncBeijingChina School of Vehicle and Mobility Tsinghua UniversityLee Shau Kee Building No A639Beijing 100084China

Updating high-definition maps is imperative for the safety of autonomous ***,positional changes in lane lines are hard to be detected in a timely manner due to a limited number of expensive surveying vehicles over a large geo-graphic ***,a novel method is proposed to detect the geometric changes of lane lines using low-cost sensors,such as consumer-grade global navigation satellite system(GNSS)hardware receivers and *** proposed framework geometric change detection using low-cost sensors(GCD-L)and algorithm change segment compare(CSC),which are based on the lane width between the curb line and the adjacent leftmost lane line,can perceive the positional changes of the leftmost lane line on highway and expressway *** effectiveness of the proposed method is verified by evaluating it on a real-world typical urban ring road *** experimental results show that 71%detected change segments are valid with only two round crowdsourced maps.

关键词： Autonomous driving HD Maps Lane line change detection

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change detection, risk assessment and mass balance of mobile dune fields near Dunhuang Oasis with optical imagery and global terrain datasets

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International Journal of Digital Earth 2020年第12期13卷 1604-1623页

作者： Chao Ding Guangcai Feng Mingsheng Liao Lu Zhang State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China School of Geosciences and Info-Physics Central South UniversityChangshaPeople’s Republic of China

Quantifying the kinematic evolution patterns of dune migration plays a major role in resisting the sand-dust disasters and evaluating the desertification *** the increment of optical observations,measuring the dune migration with dense spatial and temporal density has become *** this study,with the open-accessible Landsat-8(L8)/Sentinel-2(S2)imagery and global terrain datasets,we designed a complete workflow of inverting the dune displacement time series and velocity,identifying the potential encroachment risk,and evaluating the sediment balance of mobilized dune fields near Dunhuang *** the performance of time-series inversion,the standard deviation(STD)of measurements in stable area of raw correlation results was improved by above 40%.The displacement time series and velocity revealed out the spatial heterogeneity and seasonal/inter-annual dynamic evolution of dune ***,dune encroachment risk in the immediate vicinity of local landscapes was evaluated to highlight the unstable dune fields,and the workability of existent sand-control ***,the mass budget of around−0.001±0.025 m/yr shows the stable sediment transport in Mingsha *** study case can be exemplified to lead the risk monitoring and sustainable conservation of Dunhuang region and other places controlled by similar climatic conditions.

关键词： change detection risk assessment mass balance dune migration Dunhuang oasis

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Adaptive change detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

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Chinese Journal of Mechanical Engineering 2017年第6期30卷 1338-1346页

作者： Teng Wang Guo-Liang Lu Jie Liu Peng Yan Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of MOE School of Mechanical EngineeringShandong University Department of Mechanical and Aerospace Engineering Carleton University

detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space （RK_HS） for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor （used to control the coarse-to-fine level of detection） is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

关键词： Machine monitoring change detection Long-term monitoring Adaptive threshold

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change detection Based on DSM and Image Features in Urban Areas

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Geo-Spatial Information Science 2003年第2期6卷 35-41页

作者： LIU Zhifang ZHANG Jianqing ZHANG Zuxun FAN Hong LIU Zhifang, Ph.D candidate, Institute of Image & Graphic, Sichuan University, Chengdu 610064, China. Institute of Image & Graphic Sichuan University Chengdu China

On the basis of stereo image analysis, the change detection of man-made objects in urban areas is introduced. Information of the height of man-made objects can be applied to reinforce their change detection. By comparison between the new and old DSMs, the changed regions are extracted. However, our aim is to detect changes of man-made objects in urban area and further in the potential areas by the means of line-feature matching and gradient direction histogram. The experiments based on the aerial images from Japan have proven that the algorithm is correct and efficient.

关键词： change detection DSM gradient direction histogram image matching housedetection

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SAR polarimetric change detection for flooded vegetation

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International Journal of Digital Earth 2013年第2期6卷 103-114页

作者： B.Brisco A.Schmitt K.Murnaghan S.Kaya A.Roth Canada Centre for Remote Sensing Earth Sciences SectorNatural Resources CanadaOttawaONCanada German remote sensing data center german aerospace centeroberpfaffenhofenwesslinggermany

Due to spatial and temporal variability an effective monitoring system for water resources must consider the use of remote sensing to provide *** Aperture Radar(SAR)is useful due to timely data acquisition and sensitivity to surface water and flooded *** ability to map flooded vegetation is attributed to the double bounce scattering mechanism,often dominant for this *** Ting Lake in China is an ideal site for evaluating SAR data for this application due to annual flooding caused by mountain snow melt causing extensive changes in flooded vegetation.A curvelet-based approach for change detection in SAR imagery works well as it highlights the change and suppresses the speckle *** paper addresses the extension of this change detection technique to polarimetric SAR data for monitoring surface water and flooded ***-2 images of Dong Ting Lake demonstrate this curvelet-based change detection technique applied to wetlands although it is applicable to other land covers and for post disaster impact *** tools are important to Digital Earth for map updating and revision.

关键词： SAR polarimetry change detection wetlands floods earth observa-tion land cover microwave remote sensing

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