Self-Supervised Assisted Semi-Supervised Residual Network for Hyperspectral Image Classification

Abstract

Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep learning methods driven by massive data cannot achieve the intended expectations. Semi-supervised and self-supervised algorithms have advantages in coping with this phenomenon. This paper primarily concentrates on applying self-supervised strategies to make strides in semi-supervised HSI classification. Notably, we design an effective and a unified self-supervised assisted semi-supervised residual network (SSRNet) framework for HSI classification. The SSRNet contains two branches, i.e., a semi-supervised and a self-supervised branch. The semi-supervised branch improves performance by introducing HSI data perturbation via a spectral feature shift. The self-supervised branch characterizes two auxiliary tasks, including masked bands reconstruction and spectral order forecast, to memorize the discriminative features of HSI. SSRNet can better explore unlabeled HSI samples and improve classification performance. Extensive experiments on four benchmarks datasets, including Indian Pines, Pavia University, Salinas, and Houston2013, yield an average overall classification accuracy of 81.65%, 89.38%, 93.47% and 83.93%, which sufficiently demonstrate that SSRNet can exceed expectations compared to state-of-the-art methods.

A Novel Knowledge Distillation Method for Self-Supervised Hyperspectral Image Classification

“…Most self-supervised pretext tasks designed a priori may face ambiguity problems; for example, in rotation angle prediction where some objects do not have a usual orientation [49]. Given the advantages of self-supervised learning, a number of methods have been proposed to use self-supervised learning for HSI classification [50][51][52]. The existing methods verify the feasibility of self-supervised learning in the field of HSI classification.…”

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

“…Their feature learning and classification was based on an ensemble learning strategy which jointly utilised spatial context information at different scales and feature information in different bands. In Song et al [46], a selfsupervised branch appeared in a semi-supervised residual network (SSRNet). Discriminative features were learned by performing two tasks: masked bands reconstruction and spectral order forecast.…”

Ten deep learning techniques to address small data problems with remote sensing

“…However, we only found four papers dealing with self-supervised learning in RS using a small sample (Bing Rangnekar et al, 2020;Song et al, 2022;Zhixiang Xue et al, 2022). These were related to solving the HSI classification problem.…”