Semi-Supervised Neural Architecture Search for Hyperspectral Imagery Classification Method with Dynamic Feature Clustering

Hyperspectral image (HSI) contains rich spatial and spectral information, which makes the HSI classification task the research focus of HSI analysis within remote sensing community. Though deep learning-based HSI classification methods obtain good performance in recent years, how to learn network structure better suitable for a given HSI instead of utilizing a manually designed one for HSI classification is still a challenging problem, especially providing only a small amount of labeled samples. To address this problem, we propose the first semi-supervised HSI classification network constructed via the neural architecture search (NAS). Specifically, we propose a two-head semi-supervised HSI classification framework utilizing both labeled and unlabeled data, which consists of a shared feature extraction module, a classifier module for labeled samples together with a clustering module for unlabeled samples. To boost the performance of the constructed two-head network, we propose to utilize deep features instead of the original pixels for HSI clustering to generate pseudo-labels for the unlabeled data. Within the conducted semi-supervised network, we specifically design a method to automatically search for the shared feature extraction module better suitable for the given HSI data, which leads to better HSI classification results. Experimental results on three HSI datasets demonstrate the effectiveness of the proposed method, providing only a limited number of labeled training samples.