Deep Relation Network for Hyperspectral Image Few-Shot Classification

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 6; p. 923
• Main Authors: Gao, Kuiliang, Liu, Bing, Yu, Xuchu, Qin, Jinchun, Zhang, Pengqiang, Tan, Xiong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2020
• Subjects: Accuracy; Classification; convolutional neural network; Datasets; Deep learning; hyperspectral image few-shot classification; Hyperspectral imaging; Image classification; Learning algorithms; Machine learning; meta-learning; Methods; Modules; Neural networks; Principal components analysis; relation network; Remote sensing; Researchers; Support vector machines
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12060923

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.