Deep Mutual-Teaching for Hyperspectral Imagery Classification

Abstract

Hyperspectral imagery (HSI) classification is a widely used method in remote sensing, which can provide accurate label information for each pixel. Though the classification accuracy is very high for many publicly available data sets in many research articles, they often exhibit much worse performance in practical applications. Because most of the articles adopt a random sampling strategy to select training and test samples from the same image, the high correlation between training and test samples will bring optimistic results. However, this strategy is not suitable for practical application. Because the training and test samples are collected from different locations in most situations, in this letter, the nonoverlapped sampling is adopted to reduce the correlation between training and test samples. Four key factors are presented to analyze the HSI classification; then, a new deep mutual-teaching method is proposed to classify the HSI. The experimental results show that the performance of the proposed method outperforms comparison methods.