Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering

By Maoguo Gong, Zhiqiang Zhou, Jingjing Ma. IEEE Transactions on Image Processing, 2012

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Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016, methodology): “supervised fine-tuning stage, we can select some samples from the results obtained by the available algorithms, such as improved FCM algorithm based on Markov random field (MRFFCM) [13], GKI [1], RFLICM [5], and local fit-search model (LFS)_kernel-induced graph cuts (KGC) [11], respectively.”
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation (2013, unknown): “More recently, we [12] proposed a variant of FLICM algorithm (RFLICM), which adopts the local coefficient of variation to replace the spatial distance as a local similarity measure.”
A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images (2018, methodology): “[25], neural network-based methods [26], [27], and fuzzy clustering-based methods [28], [29].”
Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (2018, background): “Gong et al. [23] utilized a variable local coefﬁcient instead of the ﬁxed spatial distance, and proposed a variant of the FLICM algorithm (RFLICM) that is able to exploit more local context information in images.”