Learning from small data for hyperspectral image classification

Abstract

With the development of deep learning technique, many hyperspectral image classification (HIC) methods achieve great progress based on the application of convolution neural network (CNN). However, most of them still face small data cases. In this paper, we study the challenges of small data in HIC and propose a stronger HIC small data learning framework (SDLF). More specifically, a feature enhancement module named high-low dimension hybrid representation (HLDR) is designed to improve the feature differentiation power. To generate higher quality classification labels for unlabeled data, a smooth label generation module (SLG) is introduced based on Spatio-Temporal constraint. Then, we introduce a dynamically training sample amplification (TSA) under a unified curriculum learning strategy to add generated reliable labels into training samples. Finally, integrating HLDR in feature representation, SLG in label generation, and TSA in training amplification, the proposed SDLF framework is constructed, which can ensure correctness of HIC network. Experimental results demonstrate that our SDLF can not only learn robust feature representation to enhance the differentiation power but also select reliably generated labels to enrich the training data, which is useful for learning from small data. Also, our SDLF method achieves a superior performance with small labeled training data on three typical datasets.