[1] REN Qiuru, YANG Wenzhong, WANG Chuanjian et al. Review of remote sensing image change detection. Journal of Computer Applications, 41, 2294-2305(2021).

[2] GäRTNER P, FöRSTER M, KURBAN A et al. Object based change detection of Central Asian Tugai vegetation with very high spatial resolution satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 31, 110-121(2014).

[3] HUANG Shiqi, LIU Daizhi, HU Mingxing et al. Multi-temporal SAR image change detection technique based on wavelet transform. Acta Geodaetica et Cartographica Sinica, 39, 180-186(2010).

[4] LU P, QIN Y, LI Z et al. Landslide mapping from multi-sensor data through improved change detection-based Markov random field. Remote Sensing of Environment, 231, 111235(2019).

[5] GUO Qing, ZHU Liya, LI An et al. Landslide identification method based on NDVI change detection. Remote Sensing Technology and Application, 37, 17-23(2022).

[6] LIN Y, ZHANG L, WANG N. A new time series change detection method for landsat land use and land cover change, 1-4(2019).

[7] WU Tianjun, HU Xiaodong, XIA Liegang et al. Analysis on dynamic change and tendency of land-cover based on object-oriented classification. Remote Sensing Technology and Application, 29, 600-606(2014).

[8] YE Yuanxin, SUN Miaomiao, WANG Mengmeng et al. Change detection of remote sensing images by combining neighborhood information and structural features. Acta Geodaetica et Cartographica Sinica, 50, 1-9(2021).

[9] BAYARJARGAL Y, KARNIELI A, BAYASGALAN M et al. A comparative study of NOAA–AVHRR derived drought indices using change vector analysis. Remote Sensing of Environment, 105, 9-22(2006).

[10] BRUZZONE L, PRIETO D F. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38, 1171-1182(2000).

[11] TONG Guofeng, LI Yong, DING Weili et al. Review of remote sensing image change detection. Journal of Image and Graphics, 20, 1561-1571(2015).

[12] JENSEN J R, TULLIS J A. Object‐based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing, 29, 399-423(2008).

[13] DESCLéE B, BOGAERT P, DEFOURNY P. Forest change detection by statistical object-based method. Remote Sensing of Environment, 102, 1-11(2006).

[14] ZHANG P, LV Z, SHI W. Object-based spatial feature for classification of very high resolution remote sensing images. IEEE Geoscience and Remote Sensing Letters, 10, 1572-1576(2013).

[15] TABIB MAHMOUDI F, SAMADZADEGAN F, REINA-RTZ P. Context aware modification on the object based image analysis. Journal of the Indian Society of Remote Sensing, 43, 709-717(2015).

[16] CAI S, LIU D. A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images. Remote Sensing Letters, 4, 998-1007(2013).

[17] WANG Q, ZHANG X, CHEN G et al. Change detection based on Faster R-CNN for high-resolution remote sensing images. Remote Sensing Letters, 9, 923-932(2018).

[18] LU N, CHEN C, SHI W B et al. Weakly supervised change detection based on edge mapping and SDAE network in high-resolution remote sensing images. Remote Sensing, 12, 3907(2020).

[19] MOHAMMED EL AMIN L, SOULEYMAN C, BAKHTI K et al. High-resolution optical remote sensing imagery change detection through Deep Transfer Learning. Journal of Applied Remote Sensing, 13, 1-18(2019).

[20] GONG M, ZHAN T, ZHANG P et al. Superpixel-based difference representation learning for change detection in multispectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 55, 2658-2673(2017).

[21] LIU Sicong, DU Kecheng, ZHENG Yongjie et al. Remote sensing change detection technology in the Era of artificial intelligence：Inheritance，development and challenges. National Remote Sensing Bulletin, 27, 1975-1987(2023).

[22] QI Xinbo. Overview of object-oriented change detection in remote sensing images. Beijing Surveying and Mapping, 35, 427-431(2021).

[23] SHI Wenzhong, ZHANG Penglin. State-of-the-Art remotely sensed images-based change detection methods. Geomatics and Information Science of Wuhan University, 43, 1832-1837(2018).

[24] WANG Chao, WANG Shuai, CHEN Xiao et al. Object-level change detection of multi-sensor optical remote sensing images combined with UNet++ and multi-level difference module. Acta Geodaetica et Cartographica Sinica, 52, 283-296(2023).

[25] ZHANG Zuxun, JIANG Huiwei, PANG Shiyan et al. Review and prospect in change detection of multi-temporal remote sensing images. Acta Geodaetica et Cartographica Sinica, 51, 1091-1107(2022).

[26] DU Peijun, WANG Xin, MENG Yaping et al. Effective change detection approaches for geographic national condition monitoring and land cover map updating. Journal of Geo-information Science, 22, 857-866(2020).

[27] ZHAO Zhenzhen. The research of change detection method using high-resolution semote sensing images and vector data(2014).

[28] DING Yong. Research on change detection method of high-resolution remote sensing image combined with land covering classification vector data(2017).

[29] ZHANG Chunsen, LI Guojun, CUI Weihong. A change detection method for remote sensing image based on vector data. Geomatics and Information Science of Wuhan University, 46, 309-317(2021).

[30] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient graph-based image segmentation. International Journal of Computer Vision, 59, 167-181(2004).

[31] VEDALDI A, SOATTO S. Quick shift and kernel methods for mode seeking, 705-718(2008).

[32] ACHANTA R, SHAJI A, SMITH K et al. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012).

[33] IRVING B. maskSLIC： Regional superpixel generation with application to local pathology characterisation in medical images. arXiv Pre-print(2016).

[34] ARTHUR D, VASSILVITSKII S. K-means++： The advantages of careful seeding, 1027-1035(2007).

[35] HE F, PARVEZ MAHMUD M A, KOUZANI A Z et al. An improved SLIC algorithm for segmentation of microscopic cell images. Biomedical Signal Processing and Control, 73, 103464(2022).

[36] ALBREGTSEN F. Statistical texture measures computed from gray level coocurrence matrices. Image Processing Laboratory, 5, 1-14(2008).

[37] LIU Jiange, BAI Mu, WANG Xinshang. Study on deep learning classification fault tolerance based on geographical conditions. Geospatial Information, 20, 9-14(2022).

[38] LI E, SAMAT A, LIU W et al. High-resolution imagery classification based on different levels of information. Remote Sensing, 11, 2916(2019).

[39] HE K, ZHANG X, REN S et al. Deep residual learning for image recognition, 770-778(2016).

[40] TAN M, LE Q. Efficientnet： Rethinking model scaling for convolutional neural networks, 6105-6114(2019).

[41] LIN T Y, ROYCHOWDHURY A, MAJI S. Bilinear convolutional neural networks for fine-grained visual recognition. IEEE Transactions on Pattern Analysis and Machine Intellige-nce, 40, 1309-1322(2018).

[42] LI E, SAMAT A, DU P et al. Improved bilinear CNN model for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 19, 1-5(2020).

[43] CHEN S, TIAN Y. Pyramid of spatial relatons for scene-level land use classification. IEEE Transactions on Geoscience and Remote Sensing, 53, 1947-1957(2015).

[44] BALDUZZI D, FREAN M, LEARY L et al. The shattered gradients problem： If resnets are the answer， then what is the question？, 342-350(2017).

[45] LIU M Y, TUZEL O, RAMALINGAM R et al. Entropy rate superpixel segmentation, 2097-2104(2011).