空间约束及其在遥感图像信息提取中的应用研究综述

沈宇臻; 玉院和; 韦玉春; 郭厚财; 芮旭东

doi:10.11834/jrs.20222078

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空间约束及其在遥感图像信息提取中的应用研究综述
Review of spatial constraint technology application in information extraction from remote sensing images
2022年页码：1-17
DOI： 10.11834/jrs.20222078

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引用
沈宇臻，玉院和，韦玉春，郭厚财，芮旭东.XXXX.空间约束及其在遥感图像信息提取中的应用研究综述.遥感学报，XX（XX）： 1-17

SHEN Yuzhen，YU Yuanhe，WEI Yuchun，GUO Houcai，RUI Xudong. XXXX. Review of spatial constraint technology application in information extraction from remote sensing images. National Remote Sensing Bulletin， XX（XX）：1-17
沈宇臻，玉院和，韦玉春，郭厚财，芮旭东.XXXX.空间约束及其在遥感图像信息提取中的应用研究综述.遥感学报，XX（XX）： 1-17 DOI： 10.11834/jrs.20222078.

SHEN Yuzhen，YU Yuanhe，WEI Yuchun，GUO Houcai，RUI Xudong. XXXX. Review of spatial constraint technology application in information extraction from remote sensing images. National Remote Sensing Bulletin， XX（XX）：1-17 DOI： 10.11834/jrs.20222078.

摘要

遥感卫星技术的发展使得高空间分辨率的遥感图像得到了更多的应用。但是，高空间分辨率遥感图像常常具有高的类内方差，由此限制了许多遥感信息提取方法的性能。为解决此类问题，图像中像素间的空间约束成为研究热点，且陆续产生了一些研究成果。但整体上，这些成果缺少联系和系统性。鉴于此，本文基于近二十年来一百余篇的相关文献，对现有空间约束流程、应用场景和方法进行了归纳与总结，对各类方法进行了原理解释及优缺点的比较。最后，分析了空间约束方法的发展趋势，列举了空间约束研究存在的可能的不足，提出了建议参考。

Abstract

The problem of a high intra-class variance arose in the very high-spatial-resolution remote sensing images (VHR) and limited the performance of many remote sensing information extraction methods. To solve such problems, the spatial constraint (SC) among pixels of images has become a hot topic, and produced many research results, but lack of association and systematicness as a whole. This paper reviewed and summarized more than one hundred related literatures published in the past two decades, so as to provide references for further research on information extraction in VHR.There are four sections in this paper.First, SC process is divided into three stages: mining and expression of spatial information, and construction of SC, was introduced in detail. In generally, the main sources of spatial information are the neighborhood of pixels, imaging relation, prior knowledge. The expression of spatial information included mean, median, extreme, azimuth order, etc. The construction methods of SC included objective function, energy function, discriminant function, etc.Second, SC applications are divided into six scenarios: image matching, image segmentation, target detection, image classification, change detection, and others, the implementation methods and characteristics of main application scenarios are summarized. The way of SC is closely related to specific application. For example, SC is mainly used to build the descriptor and transformation in image matching, implemented by model constraint, graph construction in space, and objective function in image segmentation, target detection and image classification, and emphasized on the neighborhood between pixels and prior knowledge in change detection. The common features in these scenarios are to develop a robust, unique and representative descriptor via the geometric space information to solve the specific problems in the images.Third, SC methods are divided into six types: local templates, auxiliary reference, graph construction in space, model constraints, rule constraints, and others, and are summarized in the paper according to the implementation and principles. The advantages and disadvantages of the first five methods are compared. The results showed that the different SC methods presented the different usability in different application scenarios. (1) A local template uses the spatial information of the neighborhood and can obtain more stable information expression, so is suitable for many application scenarios, especially image classification. (2) The point constraint in auxiliary reference uses the spatial relations between feature points and often appears in image matching, line constraint focuses on the connection between the target and the linear object and is suitable for extraction of man-made objects, and surface constraint is of spatial extensibility and suitable for target detection. (3) Graph construction in space uses the multidimensional spatial information intuitively and effectively and suitable for classification in hyperspectral images. (4) Model constraints are of generalization in applications, but dependent on the specific mathematical expression. (5) Rule constraint specifies the professional applications and often are used in image classification and change detection. Fully analysis and consideration of application scenarios and specific problems still are the preconditions of SC effectively.In the last, this paper presents the development trend and the possible shortcomings in the spatial constraint research, and specific suggestions for works in the future.

关键词

空间约束遥感图像信息提取邻域辅助约束遥感变化检测目标提取地表覆盖

Keywords

spatial constraintremote sensing imageinformation extractionneighborhoodauxiliary constraintremote sensing change detectiontarget extractionland cover

references

Akcay H G and Aksoy S. 2010. BUILDING DETECTION USING DIRECTIONAL SPATIAL CONSTRAINTS//Proceedings of 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM. Honolulu, HI, USA: IEEE:1932-1935 [DOI: 10.1109/igarss.2010.5652842http://dx.doi.org/10.1109/igarss.2010.5652842]

Aksoy S and Cinbis R G. 2010. Image Mining Using Directional Spatial Constraints. Ieee Geoscience and Remote Sensing Letters, 7(1): 33-37[DOI: 10.1109/lgrs.2009.2014083http://dx.doi.org/10.1109/lgrs.2009.2014083]

Ari C and Aksoy S. 2014. Detection of Compound Structures Using a Gaussian Mixture Model With Spectral and Spatial Constraints. Ieee Transactions on Geoscience and Remote Sensing, 52(10): 6627-6638[DOI: 10.1109/tgrs.2014.2299540http://dx.doi.org/10.1109/tgrs.2014.2299540]

Balasubramani K. 2018. Physical resources assessment in a semi-arid watershed: An integrated methodology for sustainable land use planning. Isprs Journal of Photogrammetry and Remote Sensing, 142:358-379[DOI: 10.1016/j.isprsjprs.2018.03.008http://dx.doi.org/10.1016/j.isprsjprs.2018.03.008]

Byju A P, Sumbul G, Demir B and Bruzzone L. 2021. Remote-Sensing Image Scene Classification With Deep Neural Networks in JPEG 2000 Compressed Domain. Ieee Transactions on Geoscience and Remote Sensing, 59(4): 3458-3472[DOI: 10.1109/tgrs.2020.3007523http://dx.doi.org/10.1109/tgrs.2020.3007523]

Cai L P, Shi W Z, Zhang H and Hao M. 2016. Object-oriented change detection method based on adaptive multi-method combination for remote-sensing images. International Journal of Remote Sensing, 37(22): 5457-5471[DOI: 10.1080/01431161.2016.1232871http://dx.doi.org/10.1080/01431161.2016.1232871]

Cao G, Liu Y Z and Shang Y F. 2014. Automatic change detection in remote sensing images using level set method with neighborhood constraints. Journal of Applied Remote Sensing, 8:083678 [DOI: 10.1117/1.jrs.8.083678http://dx.doi.org/10.1117/1.jrs.8.083678]

Cao G, Zhou L C and Li Y P. 2016. A new change-detection method in high-resolution remote sensing images based on a conditional random field model. International Journal of Remote Sensing, 37(5): 1173-1189[DOI: 10.1080/01431161.2016.1148284http://dx.doi.org/10.1080/01431161.2016.1148284]

Cao R, Tu W, Yang C X, Li Q, Liu J, Zhu J S, Zhang Q, Li Q Q and Qiu G P. 2020. Deep learning-based remote and social sensing data fusion for urban region function recognition. Isprs Journal of Photogrammetry and Remote Sensing, 163:82-97[DOI: 10.1016/j.isprsjprs.2020.02.014http://dx.doi.org/10.1016/j.isprsjprs.2020.02.014]

Chen B, Qiu F, Wu B F and Du H Y. 2015. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty. Remote Sensing, 7(5): 5980-6004[DOI: 10.3390/rs70505980http://dx.doi.org/10.3390/rs70505980]

Chen C, Yan J N, Wang L Z, Liang D and Zhang W F. 2021a. Classification of Urban Functional Areas From Remote Sensing Images and Time-Series User Behavior Data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14:1207-1221[DOI: 10.1109/jstars.2020.3044250http://dx.doi.org/10.1109/jstars.2020.3044250]

Chen L, Zhu Q, Xie X, Hu H and Zeng H. 2018. Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features. Isprs International Journal of Geo-Information, 7(9):362[DOI: 10.3390/ijgi7090362http://dx.doi.org/10.3390/ijgi7090362]

Chen W Y, Zhao Y Y, You T F, Wang H F, Yang Y and Yang K. 2021b. Automatic Detection of Scattered Garbage Regions Using Small Unmanned Aerial Vehicle Low-Altitude Remote Sensing Images for High-Altitude Natural Reserve Environmental Protection. Environmental Science & Technology, 55(6): 3604-3611[DOI: 10.1021/acs.est.0c04068http://dx.doi.org/10.1021/acs.est.0c04068]

Chen Z Y, Wang C, Li J, Xie N C, Han Y and Du J X. 2021c. Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14:2284-2294[DOI: 10.1109/jstars.2021.3053603http://dx.doi.org/10.1109/jstars.2021.3053603]

Cheng G, Si Y J, Hong H L, Yao X W and Guo L. 2021. Cross-Scale Feature Fusion for Object Detection in Optical Remote Sensing Images. Ieee Geoscience and Remote Sensing Letters, 18(3): 431-435[DOI: 10.1109/lgrs.2020.2975541http://dx.doi.org/10.1109/lgrs.2020.2975541]

Chhapariya K, Kumar A and Upadhyay P. 2021. Kernel-Based MPCM Algorithm with Spatial Constraints and Local Contextual Information for Mapping Paddy Burnt Fields. Journal of the Indian Society of Remote Sensing, 49(8): 1743-1754 [DOI: 10.1007/s12524-021-01346-1http://dx.doi.org/10.1007/s12524-021-01346-1]

de Miguel A S, Kyba C C M, Aube M, Zamorano J, Cardiel N, Tapia C, Bennie J and Gaston K J. 2019. Colour remote sensing of the impact of artificial light at night (I): The potential of the International Space Station and other DSLR-based platforms. Remote Sensing of Environment, 224:92-103[DOI: 10.1016/j.rse.2019.01.035http://dx.doi.org/10.1016/j.rse.2019.01.035]

Deng Z P, Sun H, Zhou S L, Zhao J P, Lei L and Zou H X. 2018. Multi-scale object detection in remote sensing imagery with convolutional neural networks. Isprs Journal of Photogrammetry and Remote Sensing, 145:3-22[DOI: 10.1016/j.isprsjprs.2018.04.003http://dx.doi.org/10.1016/j.isprsjprs.2018.04.003]

Fan J C and Wang J. 2015. Polarimetric SAR Image Segmentation Based on Spatially Constrained Kernel Fuzzy C-Means Clustering\\ Proceedings of Oceans 2015 Genova. Ctr Congressi Genova, Genova, ITALY: IEEE.

Fan J W. 2017. Research on SAR Image Registration Based on Feature Point. Xian: XIDIAN University

樊建伟. 2017. 基于特征点的SAR图像配准算法研究. 西安电子科技大学

Fang J, Yuan Y, Lu X Q and Feng Y C. 2019. Robust Space-Frequency Joint Representation for Remote Sensing Image Scene Classification. Ieee Transactions on Geoscience and Remote Sensing, 57(10): 7492-7502[DOI: 10.1109/tgrs.2019.2913816http://dx.doi.org/10.1109/tgrs.2019.2913816]

Feng R Y, Zhong Y F, Xu X and Zhang L P. 2016. Adaptive Sparse Subpixel Mapping With a Total Variation Model for Remote Sensing Imagery. Ieee Transactions on Geoscience and Remote Sensing, 54(5): 2855-2872[DOI: 10.1109/tgrs.2015.2506612http://dx.doi.org/10.1109/tgrs.2015.2506612]

Foody G M. 2005. Local characterization of thematic classification accuracy through spatially constrained confusion matrices. International Journal of Remote Sensing, 26(6): 1217-1228[DOI: 10.1080/01431160512331326521http://dx.doi.org/10.1080/01431160512331326521]

Gao F, Hu L and He Z F. 2011. BRIDGE EXTRACTION BASED ON ON CONSTRAINED DELAUNAY TRIANGULATION FOR PANCHROMATIC IMAGE, 2011 Ieee International Geoscience and Remote Sensing Symposium: 1429-1432[DOI: 10.1109/igarss.2011.6049334http://dx.doi.org/10.1109/igarss.2011.6049334]

Gao L P, Shi W H, Miao Z L and Lv Z Y. 2018. Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images. Remote Sensing, 10(6)：900[DOI: 10.3390/rs10060900http://dx.doi.org/10.3390/rs10060900]

Gao Y, Cui L J, Liu J J, Li W and Lei Y R. 2020. China's coastal-wetland change analysis based on high-resolution remote sensing. Marine and Freshwater Research, 71(9): 1161-1181[DOI: 10.1071/mf19062http://dx.doi.org/10.1071/mf19062]

Hao M, Shi W Z, Ye Y X, Zhang H and Deng K Z. 2019. A novel change detection approach for VHR remote sensing images by integrating multi-scale features. International Journal of Remote Sensing, 40(13): 4910-4933[DOI: 10.1080/01431161.2019.1577576http://dx.doi.org/10.1080/01431161.2019.1577576]

He L H, Wen Y, Wan M and Liu S. 2014. Multi-channel features based automated segmentation of diffusion tensor imaging using an improved FCM with spatial constraints. Neurocomputing, 137:107-114[DOI: 10.1016/j.neucom.2013.09.051http://dx.doi.org/10.1016/j.neucom.2013.09.051]

Hu Z, Wu Z, Zhang Q, Fan Q and Xu J. 2013. A Spatially-Constrained Color-Texture Model for Hierarchical VHR Image Segmentation. Ieee Geoscience and Remote Sensing Letters, 10(1): 120-124[DOI: 10.1109/lgrs.2012.2194693http://dx.doi.org/10.1109/lgrs.2012.2194693]

Huang X, Wan X and Peng D F. 2020. Robust Feature Matching with Spatial Smoothness Constraints. Remote Sensing, 12(19):3158[DOI: 10.3390/rs12193158http://dx.doi.org/10.3390/rs12193158]

Ji R R, Gao Y, Hong R C, Liu Q, Tao D C and Li X L. 2014. Spectral-Spatial Constraint Hyperspectral Image Classification. Ieee Transactions on Geoscience and Remote Sensing, 52(3): 1811-1824[DOI: 10.1109/tgrs.2013.2255297http://dx.doi.org/10.1109/tgrs.2013.2255297]

Jiang J and Shi X L. 2016. A Robust Point-Matching Algorithm Based on Integrated Spatial Structure Constraint for Remote Sensing Image Registration. Ieee Geoscience and Remote Sensing Letters, 13(11): 1716-1720[DOI: 10.1109/lgrs.2016.2605304http://dx.doi.org/10.1109/lgrs.2016.2605304]

Jiang S and Jiang W S. 2019. Reliable image matching via photometric and geometric constraints structured by Delaunay triangulation. Isprs Journal of Photogrammetry and Remote Sensing, 153:1-20[DOI: 10.1016/j.isprsjprs.2019.04.006http://dx.doi.org/10.1016/j.isprsjprs.2019.04.006]

Jiang S, Jiang W S, Li L L, Wang L Z and Huang W. 2020. Reliable and Efficient UAV Image Matching via Geometric Constraints Structured by Delaunay Triangulation. Remote Sensing, 12(20):3390[DOI: 10.3390/rs12203390http://dx.doi.org/10.3390/rs12203390]

Jiang X L, Wang Q, He B, Chen S J and Li B L. 2016. Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints. Neurocomputing, 207:22-35[DOI: 10.1016/j.neucom.2016.03.046http://dx.doi.org/10.1016/j.neucom.2016.03.046]

Kang J Y and Zhang W J. 2019. Kernelized FCM Algorithm with Non-Local Spatial Constraint for Image Segmentation. Journal of Nanjing Normal University(Natural Science Edition), 42(03): 122-128

康家银, 张文娟. 2019. 用于图像分割的非局部空间约束的核FCM算法. 南京师大学报(自然科学版), 42(03): 122-128[DOI: 10.3969/j.issn.1001-4616.2019.03.016http://dx.doi.org/10.3969/j.issn.1001-4616.2019.03.016]

Kennedy R E, Townsend P A, Gross J E, Cohen W B, Bolstad P, Wang Y Q and Adams P. 2009. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sensing of Environment, 113(7): 1382-1396[DOI: 10.1016/j.rse.2008.07.018http://dx.doi.org/10.1016/j.rse.2008.07.018]

Khatami R, Mountrakis G and Stehman S V. 2016. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 177:89-100[DOI: 10.1016/j.rse.2016.02.028http://dx.doi.org/10.1016/j.rse.2016.02.028]

Lei J J, Luo X W, Fang L Y, Wang M Y and Gu Y F. 2020. Region-Enhanced Convolutional Neural Network for Object Detection in Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 58(8): 5693-5702[DOI: 10.1109/tgrs.2020.2968802http://dx.doi.org/10.1109/tgrs.2020.2968802]

Li S, Peng M, Zhang B, Feng X and Wu Y. 2019. Auto-registration of medium and high spatial resolution satellite images by integrating improved SIFT and spatial consistency constraints. International Journal of Remote Sensing, 40(14): 5635-5650[DOI: 10.1080/01431161.2019.1580793http://dx.doi.org/10.1080/01431161.2019.1580793]

Li X, Ling F, Du Y, Feng Q and Zhang Y. 2014. A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images. Isprs Journal of Photogrammetry and Remote Sensing, 93:76-87[DOI: 10.1016/j.isprsjprs.2014.03.013http://dx.doi.org/10.1016/j.isprsjprs.2014.03.013]

Li X G, Wang Y B, Zhang L Q, Liu S H, Mei J and Li Y. 2020a. Topology-Enhanced Urban Road Extraction via a Geographic Feature-Enhanced Network. Ieee Transactions on Geoscience and Remote Sensing, 58(12): 8819-8830[DOI: 10.1109/tgrs.2020.2991006http://dx.doi.org/10.1109/tgrs.2020.2991006]

Li Y, Xu Q Y, Lin W J and Zhao Q H. 2017. Remote Sensing Image Segmentation Method Combining Multivariate Information Clustering with Spatial Constraints. Journal of Signal Processing, 33(05): 749-757

李玉, 徐秋晔, 林文杰, 赵泉华. 2017. 结合多元信息聚类与空间约束的遥感图像分割方法. 信号处理, 33(05): 749-757[DOI: 10.16798/j.issn.1003-0530.2017.05.012http://dx.doi.org/10.16798/j.issn.1003-0530.2017.05.012]

Li Z, Jia Z H, Liu L Y, Yang J and Kasabov N. 2020b. A method to improve the accuracy of SAR image change detection by using an image enhancement method. Isprs Journal of Photogrammetry and Remote Sensing, 163:137-151[DOI: 10.1016/j.isprsjprs.2020.03.002http://dx.doi.org/10.1016/j.isprsjprs.2020.03.002]

Li Z X, Shi W Z, Hao M and Zhang H. 2017. Unsupervised change detection using spectral features and a texture difference measure for VHR remote-sensing images. International Journal of Remote Sensing, 38(23): 7302-7315[DOI: 10.1080/01431161.2017.1375616http://dx.doi.org/10.1080/01431161.2017.1375616]

Liu H F, Yang M H, Chen J, Hou J L and Deng M. 2018. Line-Constrained Shape Feature for Building Change Detection in VHR Remote Sensing Imagery. Isprs International Journal of Geo-Information, 7(10):410[DOI: 10.3390/ijgi7100410http://dx.doi.org/10.3390/ijgi7100410]

Liu Y, Cao H, Zhao Y, He Q, Yang Y, Wang L, Lin G and Zhou J. 2021. A Remote Sensing Image Registration Algorithm Based on Multiple Constraints and a Variational Bayesian Framework. Remote Sensing Letters, 12(3): 296-305[DOI: 10.1080/2150704x.2021.1884916http://dx.doi.org/10.1080/2150704x.2021.1884916]

Liu Y, Chang M and Xu J. 2020. High-Resolution Remote Sensing Image Information Extraction and Target Recognition Based on Multiple Information Fusion. Ieee Access, 8:121486-121500[DOI: 10.1109/access.2020.3006288http://dx.doi.org/10.1109/access.2020.3006288]

Liu Z X, An J B and Jing Y. 2012. A Simple and Robust Feature Point Matching Algorithm Based on Restricted Spatial Order Constraints for Aerial Image Registration. Ieee Transactions on Geoscience and Remote Sensing, 50(2): 514-527[DOI: 10.1109/tgrs.2011.2160645http://dx.doi.org/10.1109/tgrs.2011.2160645]

Lu Y J, Duan M Y and Su Y. 2020. Segmentation Method of Directed Space Gaussian Mixture Model for Remote Sensing Imagery. Remote Sensing Information, 33(06):19-24

卢印举, 段明义, 苏玉. 2020. 遥感图像的有向空间高斯混合模型分割方法. 遥感信息, 35(06): 19-24[DOI: 10.3969/j.issn.1000-3177.2020.06.003http://dx.doi.org/10.3969/j.issn.1000-3177.2020.06.003]

Lv Z Y, Liu T F and Benediktsson J A. 2020. Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 58(9): 6524-6533[DOI: 10.1109/tgrs.2020.2977248http://dx.doi.org/10.1109/tgrs.2020.2977248]

Lv Z Y, Liu T F, Zhang P L, Benediktsson J A, Lei T and Zhang X K. 2019. Novel Adaptive Histogram Trend Similarity Approach for Land Cover Change Detection by Using Bitemporal Very-High-Resolution Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 57(12): 9554-9574[DOI: 10.1109/tgrs.2019.2927659http://dx.doi.org/10.1109/tgrs.2019.2927659]

Meng F Y, Li X and Pei J H. 2015. A Feature Point Matching Based on Spatial Order Constraints Bilateral-Neighbor Vote. Ieee Transactions on Image Processing, 24(11): 4160-4171[DOI: 10.1109/tip.2015.2456633http://dx.doi.org/10.1109/tip.2015.2456633]

Meng X C, Shen H F, Zhang H Y, Zhang L P and Li H F. 2014. Maximum a Posteriori Fusion Method Based on Gradient Consistency Constraint for Multispectral/Panchromatic Remote Sensing Images. Spectroscopy and Spectral Analysis, 34(5): 1332-1337[DOI: 10.3964/j.issn.1000-0593(2014)05-1332-06http://dx.doi.org/10.3964/j.issn.1000-0593(2014)05-1332-06]

Mujica-Vargas D, Gallegos-Funes F J and Rosales-Silva A J. 2013. A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recognition Letters, 34(4): 400-413[DOI: 10.1016/j.patrec.2012.10.004http://dx.doi.org/10.1016/j.patrec.2012.10.004]

Mulder V L, de Bruin S and Schaepman M E. 2013. Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 21: 301-310[DOI: 10.1016/j.jag.2012.07.004http://dx.doi.org/10.1016/j.jag.2012.07.004]

Ng E S and Kingsbury N G. 2010. MATCHING OF INTEREST POINT GROUPS WITH PAIRWISE SPATIAL CONSTRAINTS, 2010 Ieee International Conference on Image Processing: 2693-2696[DOI: 10.1109/icip.2010.5651903http://dx.doi.org/10.1109/icip.2010.5651903]

Ni L, Gao L R, Li S S, Li J and Zhang B. 2014. Edge-constrained Markov random field classification by integrating hyperspectral image with LiDAR data over urban areas. Journal of Applied Remote Sensing, 8:085089[DOI: 10.1117/1.jrs.8.085089http://dx.doi.org/10.1117/1.jrs.8.085089]

Rau J Y, Chen L C, Liu J K and Wu T H. 2007. Dynamics monitoring and disaster assessment for watershed management using time-series satellite images. Ieee Transactions on Geoscience and Remote Sensing, 45(6): 1641-1649[DOI: 10.1109/tgrs.2007.894928http://dx.doi.org/10.1109/tgrs.2007.894928]

Sedaghat A and Ebadi H. 2015. Distinctive Order Based Self-Similarity descriptor for multi-sensor remote sensing image matching. Isprs Journal of Photogrammetry and Remote Sensing, 108:62-71[DOI: 10.1016/j.isprsjprs.2015.06.003http://dx.doi.org/10.1016/j.isprsjprs.2015.06.003]

Sedaghat A, Mokhtarzade M and Ebadi H. 2011. Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 49(11): 4516-4527[DOI: 10.1109/tgrs.2011.2144607http://dx.doi.org/10.1109/tgrs.2011.2144607]

Shen J, Chen H, Xu M, Wang C and Liu H. 2019. Intelligent image segmentation model for remote sensing applications. Journal of Intelligent & Fuzzy Systems, 37(1): 361-370[DOI: 10.3233/jifs-179092http://dx.doi.org/10.3233/jifs-179092]

Singh P P and Garg R D. 2014. Classification of high resolution satellite images using spatial constraints-based fuzzy clustering. Journal of Applied Remote Sensing, 8: 083526[DOI: 10.1117/1.jrs.8.083526http://dx.doi.org/10.1117/1.jrs.8.083526]

Stumpf A, Lachiche N, Malet J-P, Kerle N and Puissant A. 2014. Active Learning in the Spatial Domain for Remote Sensing Image Classification. Ieee Transactions on Geoscience and Remote Sensing, 52(5): 2492-2507[DOI: 10.1109/tgrs.2013.2262052http://dx.doi.org/10.1109/tgrs.2013.2262052]

Su T F. 2019. Scale-variable region-merging for high resolution remote sensing image segmentation. Isprs Journal of Photogrammetry and Remote Sensing, 147: 319-334[DOI: 10.1016/j.isprsjprs.2018.12.003http://dx.doi.org/10.1016/j.isprsjprs.2018.12.003]

Tan K, Zhang Y, Du Q, Du P, Jin X and Li J. 2018. Change Detection based on Stacked Generalization System with Segmentation Constraint. Photogrammetric Engineering and Remote Sensing, 84(11): 733-741[DOI: 10.14358/pers.84.11.733http://dx.doi.org/10.14358/pers.84.11.733]

Trias-Sanz R, Stamon G and Louchet J. 2008. Using colour, texture, and hierarchial segmentation for high-resolution remote sensing. Isprs Journal of Photogrammetry and Remote Sensing, 63(2): 156-168[DOI: 10.1016/j.isprsjprs.2007.08.005http://dx.doi.org/10.1016/j.isprsjprs.2007.08.005]

Uhl J H and Leyk S. 2020. Towards a novel backdating strategy for creating built-up land time series data using contemporary spatial constraints. Remote Sensing of Environment, 238:111197[DOI: 10.1016/j.rse.2019.05.016http://dx.doi.org/10.1016/j.rse.2019.05.016]

Vetrivel A, Gerke M, Kerle N, Nex F and Vosselman G. 2018. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning. Isprs Journal of Photogrammetry and Remote Sensing, 140: 45-59[DOI: 10.1016/j.isprsjprs.2017.03.001http://dx.doi.org/10.1016/j.isprsjprs.2017.03.001]

Wang M and Li R X. 2014. Segmentation of High Spatial Resolution Remote Sensing Imagery Based on Hard-Boundary Constraint and Two-Stage Merging. Ieee Transactions on Geoscience and Remote Sensing, 52(9): 5712-5725[DOI: 10.1109/tgrs.2013.2292053http://dx.doi.org/10.1109/tgrs.2013.2292053]

Wang M, Sun Y X and Chen G Y. 2015. Refining High Spatial Resolution Remote Sensing Image Segmentation for Man-made Objects through aCollinear and lpsilateral Neighborhood Model. Photogrammetric Engineering and Remote Sensing, 81(5): 397-406[DOI: 10.14358/pers.81.5.397http://dx.doi.org/10.14358/pers.81.5.397]

Wang Q M, Zhang C Y and Atkinson P M. 2020. Sub-pixel mapping with point constraints. Remote Sensing of Environment, 244: 111817[DOI: 10.1016/j.rse.2020.111817http://dx.doi.org/10.1016/j.rse.2020.111817]

Wang Y L and Lai H C. 2021. Improved SIFT Multispectral Remote Sensing Image Registration. Computer Simulation, 38(03):419-424

王亚丽, 赖惠成. 2021. 基于非线性空间及空间约束的遥感图像配准. 计算机仿真, 38(03): 419-424

Wang Z H, Li G and Jiang X. 2020. Flooded Area Detection Method Based on Fusion of Optical and SAR Remote Sensing Images. Journal of Radars, 9(03):539-553

王志豪, 李刚, 蒋骁. 2020. 基于光学和SAR遥感图像融合的洪灾区域检测方法. 雷达学报, 9(03): 539-553[DOI: 10.12000/JR19095http://dx.doi.org/10.12000/JR19095]

Wu C M and Yang X Q. 2020. Robust credibilistic fuzzy local information clustering with spatial information constraints. Digital Signal Processing, 97:102615[DOI: 10.1016/j.dsp.2019.102615http://dx.doi.org/10.1016/j.dsp.2019.102615]

Wu C S, Deng C B and Jia X P. 2014. Spatially Constrained Multiple Endmember Spectral Mixture Analysis for Quantifying Subpixel Urban Impervious Surfaces. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 1976-1984[DOI: 10.1109/jstars.2014.2318018http://dx.doi.org/10.1109/jstars.2014.2318018]

Wu Y Q, Shen Y and Tao F X. 2014. Remote Sensing Image Matching based on Non-subsampled Contourlet Transform and Speed Up Robust Features. Journal of Remote Sensing, 18(03):618-629

吴一全, 沈毅, 陶飞翔. 2014. 基于NSCT和SURF的遥感图像匹配. 遥感学报, 18(03): 618-629[DOI: 10.11834/jrs.20143113http://dx.doi.org/10.11834/jrs.20143113]

Xiao Z Y. 2015. Random walker with spatial constraint and hyperspectral image classification. Remote Sensing Letters, 6(4): 331-340[DOI: 10.1080/2150704x.2015.1034883http://dx.doi.org/10.1080/2150704x.2015.1034883]

Xie Y Q, Cai J N, Bhojwani R, Shekhar S and Knight J. 2020. A locally-constrained YOLO framework for detecting small and densely-distributed building footprints. International Journal of Geographical Information Science, 34(4): 777-801[DOI: 10.1080/13658816.2019.1624761http://dx.doi.org/10.1080/13658816.2019.1624761]

Yang G, Li H C, Yang W, Fu K, Celik T and Emery W J. 2019a. Variational Bayesian Change Detection of Remote Sensing Images Based on Spatially Variant Gaussian Mixture Model and Separability Criterion. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(3): 849-861[DOI: 10.1109/jstars.2019.2896233http://dx.doi.org/10.1109/jstars.2019.2896233]

Yang H L, Peng J H, Xia B R and Zhang D X. 2013. Remote Sensing Classification Using Fuzzy C-means Clustering with Spatial Constraints Based on Markov Random Field. European Journal of Remote Sensing, 46: 305-316[DOI: 10.5721/EuJRS20134617http://dx.doi.org/10.5721/EuJRS20134617]

Yang Z Q, Yang Y, Yang K and Wei Z Q. 2019b. Non-Rigid Image Registration With Dynamic Gaussian Component Density and Space Curvature Preservation. Ieee Transactions on Image Processing, 28(5): 2584-2598[DOI: 10.1109/tip.2018.2887204http://dx.doi.org/10.1109/tip.2018.2887204]

Yao F T and Qian Y T. 2009. A spatial Gaussian process method for hyperspectral remote sensing imagery classification. Journal of Nanjing University(Natural Science), 45(05): 665-670

姚伏天, 钱沄涛. 2009. 用于高光谱遥感图像分类的空间约束高斯过程方法. 南京大学学报(自然科学版), 45(05): 665-670

Yao F T, Qian Y T and Li J M. 2012. Semi-supervised learning based Gaussian processes for hyperspectral image classification. Journal of Zhejiang University(Engineering Science), 46(07): 1295-1300

姚伏天, 钱沄涛, 李吉明. 2012. 空间约束半监督高斯过程下的高光谱图像分类. 浙江大学学报(工学版), 46(07): 1295-1300[DOI: 10.3785/j.issn.1008-973X.2012.07.022http://dx.doi.org/10.3785/j.issn.1008-973X.2012.07.022]

Yao Y Q，Cheng G，Xie X X and Han J W. 2021. Optical remote sensing image object detection based on multiresolution feature fusion. National Remote Sensing Bulletin，25（5）：1124-1137

姚艳清, 程塨, 谢星星, 韩军伟. 2021. 多分辨率特征融合的光学遥感图像目标检测. 遥感学报, 25(05): 1124-1137[DOI：10.11834/jrs.20210505http://dx.doi.org/10.11834/jrs.20210505]

Ye C M, Li Y, Cui P, Liang L, Pirasteh S, Marcato J, Goncalves W N and Li J. 2019. Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(12): 5047-5060[DOI: 10.1109/jstars.2019.2951725http://dx.doi.org/10.1109/jstars.2019.2951725]

Yu H Y, Zhang X, Song M P, Hu J C, Guo Q D and Gao L R. 2020. Hyperspectral Imagery Classification Based on Multiscale Superpixel-Level Constraint Representation. Remote Sensing, 12(20):3342[DOI: 10.3390/rs12203342http://dx.doi.org/10.3390/rs12203342]

Yu L S and Liu Y M. 2016. A spatial constraint image segmentation algorithm based on block clustering, Proceedings of 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control: 695-698[DOI: 10.1109/imccc.2016.148http://dx.doi.org/10.1109/imccc.2016.148]

Yuan Y, Fang J, Lu X Q and Feng Y C. 2019. Remote Sensing Image Scene Classification Using Rearranged Local Features. Ieee Transactions on Geoscience and Remote Sensing, 57(3): 1779-1792[DOI: 10.1109/tgrs.2018.2869101http://dx.doi.org/10.1109/tgrs.2018.2869101]

Zhang A J, Yang X Z, Fang S and Ai J Q. 2020a. Region level SAR image classification using deep features and spatial constraints. Isprs Journal of Photogrammetry and Remote Sensing, 163: 36-48[DOI: 10.1016/j.isprsjprs.2020.03.001http://dx.doi.org/10.1016/j.isprsjprs.2020.03.001]

Zhang C X, Yue P, Tapete D, Jiang L C, Shangguan B Y, Huang L and Liu G C. 2020b. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. Isprs Journal of Photogrammetry and Remote Sensing, 166: 183-200[DOI: 10.1016/j.isprsjprs.2020.06.003http://dx.doi.org/10.1016/j.isprsjprs.2020.06.003]

Zhang G Y and Jia X P. 2012. Simplified Conditional Random Fields With Class Boundary Constraint for Spectral-Spatial Based Remote Sensing Image Classification. Ieee Geoscience and Remote Sensing Letters, 9(5): 856-860[DOI: 10.1109/lgrs.2012.2186279http://dx.doi.org/10.1109/lgrs.2012.2186279]

Zhang J, Rivard B and Rogge D M. 2008. The Successive Projection Algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data. Sensors, 8(2): 1321-1342[DOI: 10.3390/s8021321http://dx.doi.org/10.3390/s8021321]

Zhang X J and Wang X L. 2020. Image segmentation models of remote sensing using full residual connection andmultiscale feature fusion. Journal of Remote Sensing, 24(09):1120-1133

张小娟, 汪西莉. 2020. 完全残差连接与多尺度特征融合遥感图像分割. 遥感学报, 24(09): 1120-1133[DOI:10.11834/jrs.20208365http://dx.doi.org/10.11834/jrs.20208365]

Zhang X L, Xiao P F, Song X Q and She J F. 2013. Boundary-constrained multi-scale segmentation method for remote sensing images. Isprs Journal of Photogrammetry and Remote Sensing, 78: 15-25[DOI: 10.1016/j.isprsjprs.2013.01.002http://dx.doi.org/10.1016/j.isprsjprs.2013.01.002]

Zhang X R, Li C, Zhang J Y, Chen Q M, Feng J, Jiao L C and Zhou H Y. 2018. Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning. Remote Sensing, 10(2): 339[ DOI: 10.3390/rs10020339]

Zhao F, Jiao L C, Liu H Q and Gao X B. 2011. A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation. Signal Processing, 91(4): 988-999[DOI: 10.1016/j.sigpro.2010.10.001http://dx.doi.org/10.1016/j.sigpro.2010.10.001]

Zhao H R, Yan G J, Deng X L, Wang J D, Yang H and Li X W. 2003. A Classification Method Based on Spatial Information. Journal of Remote Sensing, 7(05):358-363

赵红蕊, 阎广建, 邓小炼, 王锦地, 杨华, 李小文. 2003. 一种简单加入空间关系的实用图像分类方法. 遥感学报, 7(05): 358-363

Zhao J W, Huang T Y and Zhou Z H. 2020. Hyperspectral image super-resolution using recursive densely convolutional neural network with spatial constraint strategy. Neural Computing & Applications, 32(18): 14471-14481[DOI: 10.1007/s00521-019-04484-3http://dx.doi.org/10.1007/s00521-019-04484-3]

Zhao Q H, Shi X, Wang Y and Li Y. 2017. Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknow class number. Journal on Communications, 38(02):34-43

赵泉华, 石雪, 王玉, 李玉. 2017. 可变类空间约束高斯混合模型遥感图像分割. 通信学报, 38(02): 34-43[DOI: 10.11959/j.issn.1000-436x.2017026http://dx.doi.org/10.11959/j.issn.1000-436x.2017026]

Zheng X, Sun X, Fu K and Wang H. 2013. Automatic Annotation of Satellite Images via Multifeature Joint Sparse Coding With Spatial Relation Constraint. Ieee Geoscience and Remote Sensing Letters, 10(4): 652-656[DOI: 10.1109/lgrs.2012.2216499http://dx.doi.org/10.1109/lgrs.2012.2216499]

Zheng X W, Hu Y F, Sun X and Wang H Q. 2014. Annotation of Remote Sensing Images Using Spatial Constrained Multi-feature Joint Sparse Coding. Journal of Electronics & Information Technology, 36(08):1891-1898

郑歆慰, 胡岩峰, 孙显, 王宏琦. 2014. 基于空间约束多特征联合稀疏编码的遥感图像标注方法研究. 电子与信息学报, 36(08): 1891-1898[DOI: 10.3724/SP.J.1146.2013.01433http://dx.doi.org/10.3724/SP.J.1146.2013.01433]

Zhong C, Wang C Z and Wu C S. 2015. MODIS-Based Fractional Crop Mapping in the US Midwest with Spatially Constrained Phenological Mixture Analysis. Remote Sensing, 7(1): 512-529[DOI: 10.3390/rs70100512http://dx.doi.org/10.3390/rs70100512]

Zhong Y, Lin X and Zhang L. 2014. A Support Vector Conditional Random Fields Classifier With a Mahalanobis Distance Boundary Constraint for High Spatial Resolution Remote Sensing Imagery. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1314-1330[DOI: 10.1109/jstars.2013.2290296http://dx.doi.org/10.1109/jstars.2013.2290296]

Zhou Y, Tu M G, Wang S X and Liu W L. 2018. A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect. Isprs International Journal of Geo-Information, 7(4): 135[DOI: 10.3390/ijgi7040135http://dx.doi.org/10.3390/ijgi7040135]

Zhu P P, Zhang L Q, Wang Y B, Mei J, Zhou G Q, Liu F Y, Liu W W and Mathiopoulos P T. 2018. Projection learning with local and global consistency constraints for scene classification. Isprs Journal of Photogrammetry and Remote Sensing, 144: 202-216[DOI: 10.1016/j.isprsjprs.2018.07.004http://dx.doi.org/10.1016/j.isprsjprs.2018.07.004]

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