A novel automatic Siamese neural network for hyperspectral image classification

Abstract

Hyperspectral image is gradually becoming the main medium in satellite observations. It provides rich spectral information of different channels to help explore various gas states. Its classification problem is among the most significant tasks on the hyperspectral image processing and application. But the classification often faces the difficulty of imbalanced data and differentiated intra-class/inter-class difference. A lot of deep learning methods have attempted to solve this problem by designing different kinds of networks, but they suffer from the query of whether the network is optimal. This paper provides an automatically framework to deal with the above problem. First of all, we introduced siamese framework to reduce the impact of imbalanced data and various intra-class/inter-class difference. Then, we designed a multi-branch search space with several convolutional operators, especially a gradient-based updating rules to search the architecture. After using ”time [Formula: see text] performance” criterion, we obtain the model on two public hyperspectral image dataset. The experimental results outperformed all other compared algorithms.