Advances in deep neural network-based hyperspectral image classification and feature learning with limited samples: a survey

Abstract

Advancements in sensor technologies have brought about significant improvements in the resolution and quality of imagery by enhancing spatial, temporal, spectral, and radiometric aspects. These remarkable progressions have sparked enhancements in hyperspectral image classification (HSIC) applications, including land cover mapping, vegetation classification, urban monitoring, and resource understanding, which are crucial for optimal earth resource management. Effective HSIC demands advanced algorithms that exhibit high accuracy, low computational complexity, and robustness in extracting intricate spectral-spatial features. The advent of deep convolutional neural networks (DCNNs) has revolutionized image classification, introducing robust architectures that continue to evolve. However, a notable challenge remains in supervised HSIC due to the scarcity of training samples, a bottleneck that has yet to be comprehensively addressed in the literature. To catalyze further exploration, this study reviews existing methods designed to mitigate the limitations posed by limited labeled data. It also examines current techniques for feature learning in HSIC using DCNNs. Additionally, the study presents results obtained from various methods applied to the most widely recognized public HSIC datasets, accompanied by insightful observations that lay the groundwork for future research endeavors within the hyperspectral community.