Nearest Neighboring Self-Supervised Learning for Hyperspectral Image Classification

Abstract

Recently, state-of-the-art classification performance of natural images has been obtained by self-supervised learning (S2L) as it can generate latent features through learning between different views of the same images. However, the latent semantic information of similar images has hardly been exploited by these S2L-based methods. Consequently, to explore the potential of S2L between similar samples in hyperspectral image classification (HSIC), we propose the nearest neighboring self-supervised learning (N2SSL) method, by interacting between different augmentations of reliable nearest neighboring pairs (RN2Ps) of HSI samples in the framework of bootstrap your own latent (BYOL). Specifically, there are four main steps: pretraining of spectral spatial residual network (SSRN)-based BYOL, generation of nearest neighboring pairs (N2Ps), training of BYOL based on RN2P, final classification. Experimental results of three benchmark HSIs validated that S2L on similar samples can facilitate subsequent classification. Moreover, we found that BYOL trained on an un-related HSI can be fine-tuned for classification of other HSIs with less computational cost and higher accuracy than training from scratch. Beyond the methodology, we present a comprehensive review of HSI-related data augmentation (DA), which is meaningful to future research of S2L on HSIs.

Renal Pathological Image Classification Based on Contrastive and Transfer Learning

“…The evaluation of the model could be improved by incorporating a wider range of contrastive learning algorithms. BYOL is a generic contrastive learning algorithm, and its advantages over other algorithms have been extensively proved [23,31,51]. Future studies should be performed to develop a specific contrastive learning algorithm in renal pathological image analysis.…”

“…Performance improvement: BYOL has demonstrated superior performance [31] and robustness to batch size variations [32] on ImageNet compared to several contrastive learning methods, including SimCLR and MoCo.…”