Change Detection Method for High Resolution Remote Sensing Images Using Deep Learning

doi:10.11947/j.AGCS.2017.20170036

Abstract

Abstract: A novel change detection method is proposed based on deep learning to improve the accuracy of change detection in very high spatial resolution remote sensing images. On the base of image pre-processing, spectral and texture changes are extracted by modified change vector analysis and grey level co-occurrence matrix respectively, both concerning spatial-contextual information. Most likely changed and unchanged pixel-pairs are obtained by an adaptive threshold for selecting the labeled samples. The proposed model based on Gaussian-Bernoulli deep Boltzmann machines with a label layer is built to learn high-level features and is trained for determining the change areas. Experimental results on WorldView-3 and Pléiades-1 show that the proposed method out performs the compared methods in the accuracy of change detection.

Key words: change detection, high resolution remote sensing, deep learning

CLC Number:

P237

ZHANG Xinlong, CHEN Xiuwan, LI Fei, YANG Ting. Change Detection Method for High Resolution Remote Sensing Images Using Deep Learning[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(8): 999-1008.

References

[1] SINGH A. Review Article Digital Change Detection Techniques Using Remotely-sensed Data[J]. International Journal of Remote Sensing, 1989, 10(6):989-1003.
[2] LEICHTLE T, GEIβ C, WURM M, et al. Unsupervised Change Detection in VHR Remote Sensing Imagery:An Object-based Clustering Approach in a Dynamic Urban Environment[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 54:15-27.
[3] TARANTINO C, ADAMO M, LUCAS R, et al. Detection of Changes in Semi-natural Grasslands by Cross Correlation Analysis with WorldView-2 Images and New Landsat 8 Data[J]. Remote Sensing of Environment, 2016, 175:65-72.
[4] FYTSILIS A L, PROKOS A, KOUTROUMBAS K D, et al. A Methodology for Near Real-time Change Detection between Unmanned Aerial Vehicle and Wide Area Satellite Images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 119:165-186.
[5] 冯文卿, 张永军. 利用多尺度融合进行面向对象的遥感影像变化检测[J]. 测绘学报, 2015, 44(10):1142-1151. DOI:10.11947/j.AGCS.2015.20140260. FENG Wenqing, ZHANG Yongjun. Object-oriented Change Detection for Remote Sensing Images Based on Multi-scale Fusion[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(10):1142-1151. DOI:10.11947/j.AGCS.2015.20140260.
[6] INGLADA J, MERCIER G. A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5):1432-1445.
[7] ALBERGA V. Similarity Measures of Remotely Sensed Multi-sensor Images for Change Detection Applications[J]. Remote Sensing, 2009, 1(3):122-143.
[8] ZHANG Puzhao, GONG Maogou, SU Linzhi, et al. Change Detection Based on Deep Feature Representation and Mapping Transformation for Multi-spatial-resolution Remote Sensing Images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116:24-41.
[9] JOHNSON R D, KASISCHKE E S. Change Vector Analysis:A Technique for the Multispectral Monitoring of Land Cover and Condition[J]. International Journal of Remote Sensing, 1998, 19(3):411-426.
[10] THONFELD F, FEILHAUER H, BRAUN M, et al. Robust Change Vector Analysis (RCVA) for Multi-sensor Very High Resolution Optical Satellite Data[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 50:131-140.
[11] HARALICK R M, SHANMUGAM K, DINSTEIN I H. Texture Features for Image Classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6):610-621.
[12] XIAO Pengfeng, ZHANG Xueliang, WANG Dongguang, et al. Change Detection of Built-up Land:A Framework of Combining Pixel-based Detection and Object-based Recognition[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 119:402-414.
[13] 阚希, 张永宏, 曹庭, 等. 利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识[J]. 测绘学报, 2016, 45(10):1210-1221. DOI:10.11947/j.AGCS.2016.20160183. KAN Xi, ZHANG Yonghong, CAO Ting, et al. Snow Cover Recognition for Qinghai-Tibetan Plateau Using Deep Learning and Multispectral Remote Sensing[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(10):1210-1221. DOI:10.11947/j.AGCS.2016.20160183.
[14] SALAKHUTDINOV R, HINTON G. Deep Boltzmann Machines[C]//Proceedings of the 12th International Conference on Artificial Intelligence and Statistics.[S.l.]:PMLR, 2009, 5:448-455.
[15] FISCHER A, IGEL C. An Introduction to Restricted Boltzmann Machines[M]//ALVAREZ L, MEJAIL M, GOMEZ L, et al. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Berlin:Springer, 2012:14-36.
[16] 刘凯, 张立民, 孙永威, 等. 利用深度玻尔兹曼机与典型相关分析的自动图像标注算法[J]. 西安交通大学学报, 2015, 49(6):33-38. LIU Kai, ZHANG Limin, SUN Yongwei, et al. An Automatic Image Annotation Algorithm Using Deep Boltzmann Machine and Canonical Correlation Analysis[J]. Journal of Xi'an Jiaotong University, 2015, 49(6):33-38.
[17] SALAKHUTDINOV R, TENENBAUM J B, TORRALBA A. Learning with Hierarchical-deep Models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8):1958-1971.
[18] YU Yongtao, GUAN Haiyan, ZAI Dawei, et al. Rotation-and-scale-invariant Airplane Detection in High-resolution Satellite Images Based on Deep-Hough-Forests[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 112:50-64.
[19] HINTON G E. Training Products of Experts by Minimizing Contrastive Divergence[J]. Neural Computation, 2002, 14(8):1771-1800.
[20] HINTON G E, SALAKHUTDINOV R R. Reducing the Dimensionality of Data with Neural Networks[J]. Science, 2006, 313(5786):504-507.
[21] 潘广源, 柴伟, 乔俊飞. DBN网络的深度确定方法[J]. 控制与决策, 2015, 30(2):256-260. PAN Guangyuan, CHAI Wei, QIAO Junfei. Calculation for Depth of Deep Belief Network[J]. Control and Decision, 2015, 30(2):256-260.
[22] IM J, JENSEN J R, TULLIS J A. Object-based Change Detection Using Correlation Image Analysis and Image Segmentation[J]. International Journal of Remote Sensing, 2008, 29(2):399-423.
[23] GHAFFARIAN S, GHAFFARIAN S. Automatic Building Detection Based on Purposive Fast ICA (PFICA) Algorithm Using Monocular High Resolution Google Earth Images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 97:152-159.
[24] 李亮, 舒宁, 王凯, 等. 融合多特征的遥感影像变化检测方法[J]. 测绘学报, 2014, 43(9):945-953. DOI:10.13485/j.cnki.11-2089.2014.0138. LI Liang, SHU Ning, WANG Kai, et al. Change Detection Method for Remote Sensing Images Based on Multi-features Fusion[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(9):945-953. DOI:10.13485/j.cnki.11-2089.2014.0138.
[25] 段光耀, 宫辉力, 李小娟, 等. 结合特征分量构建和面向对象方法提取高分辨率卫星影像阴影[J]. 遥感学报, 2014, 18(4):760-770. DUAN Guangyao, GONG Huili, LI Xiaojuan, et al. Shadow Extraction Based on Characteristic Components and Object-oriented Method for High-resolution Images[J]. Journal of Remote Sensing, 2014, 18(4):760-770.