Hyperspectral Image Classification Based on Unsupervised Regularization

Abstract

Due to the powerful feature expression ability of deep learning and its end-to-end non-linear mapping relationship, deep learning-based methods have become the mainstream method for hyperspectral image classification tasks. However, the accuracy of deep learning methods greatly depends on the use of a large number of labeled samples to train the model. Also, hyperspectral images have few labeled samples and unbalanced categories, which makes the depth model prone to overfitting, which seriously affects the classification accuracy. Therefore, how to alleviate the overfitting phenomenon caused by small samples in the classification problem based on deep learning is still a problem that needs to be solved. Considering that it is relatively easier to obtain a large number of unlabeled samples in the field of remote sensing, making full use of the unsupervised information learned from unlabeled data can regularize the supervised classification model, which can effectively alleviate the overfitting phenomenon caused by the small samples problem. In the supervised training process, unsupervised information from the overall distribution of the sample is introduced to guide the regularization of the model, so as to realize the effective classification of the data in the case of a small number of labeled samples. Experimental results demonstrate the effectiveness of the proposed method in terms of hyperspectral image (HSI) classification with few training samples.