[1] Hussain M, Chen D M, Cheng A et al. Change detection from remotely sensed images: from pixel-based to object-based approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91-106(2013).
[2] Guimarães N, Pádua L, Marques P et al. Forestry remote sensing from unmanned aerial vehicles: a review focusing on the data, processing and potentialities[J]. Remote Sensing, 12, 1046(2020).
[3] Jiang K, Wang Z Y, Yi P et al. Edge-enhanced GAN for remote sensing image superresolution[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 5799-5812(2019).
[4] Fu Q, Guo C, Luo W L. Land use change detection based on GF-1 satellite remote sensing images[J]. Laser & Optoelectronics Progress, 57, 162802(2020).
[5] Lyu H B, Lu H, Mou L C. Learning a transferable change rule from a recurrent neural network for land cover change detection[J]. Remote Sensing, 8, 506(2016).
[6] Rokni K, Ahmad A, Selamat A et al. Water feature extraction and change detection using multitemporal Landsat imagery[J]. Remote Sensing, 6, 4173-4189(2014).
[7] Huang X, Zhu T T, Zhang L P et al. A novel building change index for automatic building change detection from high-resolution remote sensing imagery[J]. Remote Sensing Letters, 5, 713-722(2014).
[8] Wang L T, Wang S X, Zhou Y et al. Remote sensing change detection method based on texture characteristic in natural disaster monitoring[J]. Journal of Catastrophology, 29, 97-101(2014).
[9] Xin F F, Jiao L C, Wang G T et al. Change detection in multi-temporal remote sensing images based on the wavelet-domain hidden Markov chain model[J]. Journal of Xidian University, 39, 43-49(2012).
[10] Shi X F, Shuai M Q, Shen J S. Study on the intelligent change detection methods on the basis of multi-temporal remotely sensed images[J]. Bulletin of Surveying and Mapping, 23-25(2012).
[11] Peng D F, Zhang Y J, Guan H Y. End-to-end change detection for high resolution satellite images using improved UNet++[J]. Remote Sensing, 11, 1382(2019).
[12] Fu Q, Luo W L, Lü J X. Land utilization change detection of satellite remote sensing image based on AlexNet and support vector machine[J]. Laser & Optoelectronics Progress, 57, 172802(2020).
[13] Zhang P Z, Gong M G, Su L Z 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, 116, 24-41(2016).
[14] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 3-19(2018).
[15] Zhang C J, An R, Ma L. Building change detection in remote sensing image based on improved U-Net[J]. Computer Engineering and Applications, 57, 239-246(2021).
[16] Hu L, Jiang Y, Li J et al. Change detection method for high-resolution remote sensing image based on multi-scale and sparse convolution[J]. Journal of Chinese Computer Systems, 41, 2365-2370(2020).
[17] Chen L, Guan S S. Research on urban high-resolution remote sensing image changing detection method based on deep learning[J]. Application Research of Computers, 37, 320-323(2020).
[18] Xiang Y, Zhao Y D, Dong J H. Remote sensing image mining area change detection based on improved UNet siamese network[J]. Journal of China Coal Society, 44, 3773-3780(2019).
[19] Zhang Z H, Fang W, Du L L et al. Semantic segmentation of remote sensing image based on encoder-decoder convolutional neural network[J]. Acta Optica Sinica, 40, 0310001(2020).
[20] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).
[21] Xu B B, Yang F, Yang J F et al. SPNet: superpixel pyramid network for scene parsing[C], 3690-3695(2018).
[22] Ji S P, Wei S Q, Lu M. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 574-586(2019).
[23] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015, 11211, 234-241(2015).
[24] Varghese A, Gubbi J, Ramaswamy A et al. ChangeNet: a deep learning architecture for visual change detection[M]. Leal-Taixé L, Roth S. Computer vision-ECCV 2018 workshops, 11130, 129-145(2019).
[25] Sakurada K, Shibuya M, Wang W M. Weakly supervised silhouette-based semantic scene change detection[C], 6861-6867(2020).