Deep Self-Supervised Learning for Few-Shot Hyperspectral Image Classification

Abstract

Despite the success of deep learning based methods for hyperspectral imagery (HSI) classification, they demand amounts of labeled samples for training whereas the labeled samples in lots of applications are always insufficient due to the expensive manual annotation cost. To address this problem, we propose a two-branch deep learning based method for few-shot HSI classification, where two branches separately accomplish HSI classification in a cube-wise level and a cube-pair level. With a shared feature extractor sub-network, the self-supervised knowledge contained in the cube-pair branch provides an effective way to regularize the original few-shot HSI classification branch (i.e., cube-wise branch) with limited labeled samples, which thus improves the performance of HSI classification. The superiority of the proposed method on few-shot HSI classification is demonstrated experimentally on two HSI benchmark datasets.