[1] Xu Y, Feng M R, Pi J T et al. Remote sensing image segmentation method based on deep learning model[J]. Journal of Computer Applications, 39, 2905-2914(2019).

[2] Du Z X, Yin J Y, Yang J. Remote sensing image detection based on dense connected networks[J]. Laser & Optoelectronics Progress, 56, 222803(2019).

[3] Yao J H, Wu J M, Yang Y et al. Segmentation in multi-spectral remote sensing images using the fully convolutional neural network[J]. Journal of Image and Graphics, 25, 180-192(2020).

[4] Lin W J, Li Y, Zhao Q H. High-resolution remote sensing image segmentation using minimum spanning tree tessellation and RHMRF-FCM algorithm[J]. Acta Geodaetica et Cartographica Sinica, 48, 64-74(2019).

[5] Xu K, Yang W, Liu G et al. Unsupervised satellite image classification using Markov field topic model[J]. IEEE Geoscience and Remote Sensing Letters, 10, 130-134(2013).

[6] Wang Y J, Qi Q W, Liu Y. Unsupervised segmentation evaluation using area-weighted variance and Jeffries-Matusita distance for remote sensing images[J]. Remote Sensing, 10, 1193-1217(2018). http://www.ingentaconnect.com/content/doaj/20724292/2018/00000010/00000008/art00023

[7] Guo F F, Wang X X, Shen J. Adaptive fuzzy C-means algorithm based on local noise detecting for image segmentation[J]. IET Image Processing, 10, 272-279(2016). http://ieeexplore.ieee.org/document/7437559/

[8] Liu H C, Dong A G. Hyperspectral remote sensing image classification algorithm based on nonlocal mode feature fusion[J]. Laser & Optoelectronics Progress, 57, 061017(2020).

[9] Kersten P R, Lee J S, Ainsworth T L. Unsupervised classification of polarimetric synthetic aperture Radar images using fuzzy clustering and EM clustering[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 519-527(2005). http://ieeexplore.ieee.org/document/1396324/citations

[10] Hung C C, Kulkarni S, Kuo B C. A new weighted fuzzy C-means clustering algorithm for remotely sensed image classification[J]. IEEE Journal of Selected Topics in Signal Processing, 5, 543-553(2011).

[11] Wemmert C, Puissant A, Forestier G et al. Multiresolution remote sensing image clustering[J]. IEEE Geoscience and Remote Sensing Letters, 6, 533-537(2009).

[12] Kang J Y, Zhang W J. Kernelized FCM algorithm with non-local spatial constraint for image segmentation[J]. Journal of Nanjing Normal University (Natural Science Edition), 42, 122-128(2019).

[13] Dai Y, Yi B S, Xiao J S et al. Object detection of remote sensing image based on improved rotation region proposal network[J]. Acta Optica Sinica, 40, 0111020(2020).

[14] Dunn J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 3, 32-57(1973).

[15] Ahmed M N, Yamany S M, Mohamed N et al. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging, 21, 193-199(2002).

[16] Chen S C, Zhang D Q. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34, 1907-1916(2004).

[17] Szilagyi L, Benyo Z, Szilagyi S M et al. MR brain image segmentation using an enhanced fuzzy C-means algorithm[C]. //Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), September 17-21, 2003, Cancun, Mexico., 724-726(2003).

[18] Cai W L, Chen S C, Zhang D Q. Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 40, 825-838(2007). http://www.sciencedirect.com/science/article/pii/S0031320306003451

[19] Wang Z M, Soh Y C, Song Q et al. Adaptive spatial information-theoretic clustering for image segmentation[J]. Pattern Recognition, 42, 2029-2044(2009).

[20] Krinidis S, Chatzis V. A robust fuzzy local information C-means clustering algorithm[J]. IEEE Transactions on Image Processing, 19, 1328-1337(2010). http://www.ncbi.nlm.nih.gov/pubmed/20089475

[21] Verma H, Agrawal R K, Sharan A. An improved intuitionistic fuzzy C-means clustering algorithm incorporating local information for brain image segmentation[J]. Applied Soft Computing, 46, 543-557(2016). http://dl.acm.org/doi/10.1016/j.asoc.2015.12.022

[22] Lan R, Lin Y. Suppressed non-local spatial intuitionistic fuzzy C-means image segmentation algorithm[J]. Journal of Electronics & Information Technology, 41, 1472-1479(2019).

[23] Gong M G, Liang Y, Shi J et al. Fuzzy C-means clustering with local information and kernel metric for image segmentation[J]. IEEE Transactions on Image Processing, 22, 573-584(2013). http://dl.acm.org/citation.cfm?id=2711880

[24] Li N, Huo H, Zhao Y M et al. A spatial clustering method with edge weighting for image segmentation[J]. IEEE Geoscience and Remote Sensing Letters, 10, 1124-1128(2013). http://ieeexplore.ieee.org/document/6416919/