Hyperspectral Image Classification Based on Local Low-Rank Representation

Hyperspectral image classification is an attractive and challenging task due to the difficulty to acquire large labeled datasets, and its susceptibility to natural environmental influences. Deep learning-based methods can effectively enhance the efficiency of hyperspectral feature learning. Tensor-based approaches can effectively help preserving the inherent structural information. This paper introduces a novel tensor-based framework for hyperspectral image classification. It leverages the ‘bot-tleneck’ structure of an AutoEncoder to extract low-dimensional representations in a self-supervised manner. Additionally, local low-rank constraints in the embedding space facilitate the distribution of features across a low-rank manifold. Experiments conducted on real hyperspectral datasets demonstrate that the proposed method yields superior classification performance.