Fast Projected Fuzzy Clustering with Anchor Guidance for Multimodal Remote Sensing Imagery

Multimodal remote sensing image recognition is a popular research topic in the field of remote sensing. This recognition task is mostly solved by supervised learning methods that heavily rely on manually labeled data. When the labels are absent, the recognition is challenging for the large data size, complex land-cover distribution and large modality spectrum variation. In this paper, a novel unsupervised method, named fast projected fuzzy clustering with anchor guidance (FPFC), is proposed for multimodal remote sensing imagery. Specifically, according to the spatial distribution of land covers, meaningful superpixels are obtained for denoising and generating high-quality anchor. The denoised data and anchors are projected into the optimal subspace to jointly learn the shared anchor graph as well as the shared anchor membership matrix from different modalities in an adaptively weighted manner to accelerate the clustering process. Finally, the shared anchor graph and shared anchor membership matrix are combined to derive clustering labels for all pixels. An effective alternating optimization algorithm is designed to solve the proposed formulation. This is the first attempt to propose a soft clustering method for large-scale multimodal remote sensing data. Experiments show that the proposed FPFC achieves 81.34%, 55.43% and 93.34% clustering accuracies on the three datasets and outperforms the state-of-the-art methods. The source code is released at https://github.com/ZhangYongshan/FPFC.