S2JO: Spatial-Spectral Joint Optimization for Hyperspectral Image Classification With Noisy Labels

Hyperspectral image (HSI) annotation often suffers from noisy labels, which brings challenges in classification models training. Existing methods typically address this issue through two separate stages: noisy labels cleaning and robust model design. However, model performance is constrained by insufficient noisy label filtering. To explore an end-to-end joint optimization framework for HSI classification with noisy labels, we propose a spatial-spectral joint optimization (S2JO) network. The S2JO consists of a spatial nonuniform sampling (SNS) module and a spectral prototypes learning (SPL) module. The SNS module filters noisy labels dynamically by picking out the top-N confident points, purifying the input samples for the network. Meanwhile, the SPL module uses multicenter spectral prototypes to extract discriminate features accurately even if the input contains some noisy labels. Extensive experiments on the C2Seg-Beijing HSI subdataset demonstrate the superiority of the proposed S2JO over other state-of-the-art methods.