Change detection of remote sensing images by combining neighborhood information and structural features

doi:10.11947/j.AGCS.2021.20200130

Abstract

Abstract: In order to improve the accuracy of pixel-level change detection methods, this paper proposes a novel change detection method for remote sensing images by combining neighborhood information (including the neighborhood correlation image (NCI) and matching errors) and structural features. First, a technique of neighborhood correlation analysis is used to obtain the NCI which represents the context information, and the cross-correlation of neighborhood pixels is used to obtain matching errors by a template matching scheme. Then, structure features of images are extracted using orientated gradient information, which are robust to spectral differences between images. Subsequently, the initial change detection results is obtained by using the NCI, the matching errors, and structural features as the classification attributes of a decision tree. Finally, the Markov Random Field (MRF) is used to optimize the results, yielding the final binary map. The proposed method has been evaluated with two sets of bi-temporal remote sensing images from different sensors. Experimental results demonstrate that this method effectively improves the accuracy of change detection compared with the change vector analysis method, the single neighborhood information method and the method combining neighborhood information and texture features.

Key words: neighborhood information, matching errors, structural features, change detection

CLC Number:

P237

YE Yuanxin, SUN Miaomiao, WANG Mengmeng, TAN Xin. Change detection of remote sensing images by combining neighborhood information and structural features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10): 1349-1357.

References

[1] 张良培, 武辰. 多时相遥感影像变化检测的现状与展望[J]. 测绘学报, 2017, 46(10):1447-1459. DOI:10.11947/j.AGCS.2017.20170340. ZHANG Liangpei, WU Chen. Advance and future development of change detection for multi-temporal remote sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1447-1459. DOI:10.11947/j.AGCS.2017.20170340.
[2] COPPIN P, JONCKHEERE I, NACKAERTS K, et al. Review article digital change detection methods in ecosystem monitoring:a review[J]. International Journal of Remote Sensing, 2004, 25(9):1565-1596.
[3] 佟国峰, 李勇, 丁伟利, 等. 遥感影像变化检测算法综述[J]. 中国图象图形学报, 2015, 20(12):1561-1571. TONG Guofeng, LI Yong, DING Weili, et al. Review of remote sensing image change detection[J]. Journal of Image and Graphics, 2015, 20(12):1561-1571.
[4] 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, 959. 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, 959.
[5] 赵敏, 赵银娣. 面向对象的多特征分级CVA遥感影像变化检测[J]. 遥感学报, 2018, 22(1):119-131. ZHAO Min, ZHAO Yindi. Object-oriented and multi-feature hierarchical change detection based on CVA for high-resolution remote sensing imagery[J]. Journal of Remote Sensing, 2018, 22(1):119-131.
[6] 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.
[7] 眭海刚, 冯文卿, 李文卓, 等. 多时相遥感影像变化检测方法综述[J]. 武汉大学学报(信息科学版), 2018, 43(12):1885-1898. SUI Haigang, FENG Wenqing, LI Wenzhuo, et al. Review of change detection methods for multi-temporal remote sensing imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):1885-1898.
[8] BRUZZONE L, PRIETO D F. Automatic analysis of the difference image for unsupervised change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(3):1171-1182.
[9] BAYARJARGAL Y, KARNIELI A, BAYASGALAN M, et al. A comparative study of NOAA-AVHRR derived drought indices using change vector analysis[J]. Remote Sensing of Environment, 2006, 105(1):9-22.
[10] PENG Daifeng, ZHANG Yongjun, GUAN Haiyan. End-to-end change detection for high resolution satellite images using improved UNet++[J]. Remote Sensing, 2019, 11(11):1382.
[11] MOU Lichao, BRUZZONE L, ZHU Xiao xiang. Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2):924-935.
[12] MESQUITA D B, SANTOS R F D, MACHARET D G, et al. Fully convolutional Siamese autoencoder for change detection in UAV aerial images[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(8):1455-1459.
[13] 张鑫龙, 陈秀万, 李飞, 等. 高分辨率遥感影像的深度学习变化检测方法[J]. 测绘学报, 2017, 46(8):999-1008. DOI:10.11947/j.AGCS.2017.20170036. ZHANG Xinlong, CHEN Xiuwan, LI Fei, et al. Change detection method for high resolution remote sensing images using deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(8):999-1008. DOI:10.11947/j.AGCS.2017.20170036.
[14] 金秋含, 王阳萍, 杨景玉. 基于密度引力和多尺度多特征融合的遥感影像变化检测[J]. 激光与光电子学进展, 2019, 56(12):71-80. JIN Qiuhan, WANG Yangping, YANG Jingyu. Remote sensing image change detection based on density attraction and multi-scale and multi-feature fusion[J]. Laser & Optoelectronics Progress, 2019, 56(12):71-80.
[15] IM J, JENSEN J R. A change detection model based on neighborhood correlation image analysis and decision tree classification[J]. Remote Sensing of Environment, 2005, 99(3):326-340.
[16] LI Zhenxuan, SHI Wenzhong, HAO Ming, et al. Unsupervised change detection using spectral features and a texture difference measure for VHR remote-sensing images[J]. International Journal of Remote Sensing, 2017, 38(23):7302-7315.
[17] LÜ Zhiyong, LIU Tongfei, SHI Cheng, et al. Novel land cover change detection method based on k-means clustering and adaptive majority voting using bitemporal remote sensing images[J]. IEEE Access, 2019, 7:34425-34437.
[18] LÜ Zhiyong, LIU Tongfei, ZHANG Penglin, et al. Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12):9554-9574.
[19] LÜ Zhiyong, LIU Tongfei, ZHANG Penglin, et al. Land cover change detection based on adaptive contextual information using Bi-temporal remote sensing images[J]. Remote Sensing, 2018, 10(6):901.
[20] YE Yuanxin, BRUZZONE L, SHAN Jie, et al. Fast and robust matching for multimodal remote sensing image registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11):9059-9070.
[21] ZHANG Xiaokang, SHI Wenzhong, HAO Ming, et al. Level set incorporated with an improved MRF model for unsupervised change detection for satellite images[J]. European Journal of Remote Sensing, 2017, 50(1):202-210.
[22] 许妙忠, 丛铭, 万丽娟, 等. 视觉感受与Markov随机场相结合的高分辨率遥感影像分割法[J]. 测绘学报, 2015, 44(2):198-205, 213. DOI:10.11947/j.AGCS.2015.20130453. XU Miaozhong, CONG Ming, WAN Lijuan, et al. A methodology of image segmentation for high resolution remote sensing image based on visual system and Markov random field[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(2):198-205, 213. DOI:10.11947/j.AGCS.2015.20130453.
[23] 王琰, 舒宁, 龚龑. 利用马尔可夫随机场图模型的变化像斑类别判定方法[J]. 武汉大学学报(信息科学版), 2012, 37(5):542-545, 632. WANG Yan, SHU Ning, GONG Yan. Determination of new class properties of the changed image segments using MRF graph model[J]. Geomatics and Information Science of Wuhan University, 2012, 37(5):542-545, 632.
[24] 范奎奎, 王中元, 欧阳斯达, 等. DT-CWT结合MRF的遥感图像变化检测[J]. 遥感学报, 2017, 21(3):375-385. FAN Kuikui, WANG Zhongyuan, OUYANG Sida, et al. Change detection of remote sensing images through DT-CWT and MRF[J]. Journal of Remote Sensing, 2017, 21(3):375-385.
[25] HE Pengfei, SHI Wenzhong, MIAO Zelang, et al. Advanced Markov random field model based on local uncertainty for unsupervised change detection[J]. Remote Sensing Letters, 2015, 6(9):667-676.