SRTE-Net: Spectral-Spatial Similarity Reduction and Reorganized Texture Encoding for Hyperspectral Video Tracking

Hyperspectral video tracking poses unique challenges due to the high dimensionality of spectral data and the limited capacity to capture discriminative texture information. To address this, we propose a novel tracking framework that integrates spectral-spatial similarity reduction with reorganized texture encoding for robust hyperspectral target tracking. Specifically, we introduce a dimensionality compression strategy that converts the multi-band hyperspectral input into a representative grayscale image, preserving key spectral-spatial cues. To enhance discriminative texture modeling, a 3D Gabor filter is applied to the search region, and the extracted responses are adaptively fused based on their local variance. The resulting texture representations are selectively masked to suppress background noise and are then passed into a correlation filter module for precise target localization. Furthermore, we design a template update mechanism that mitigates model drift and cumulative errors during tracking. Extensive experiments on public hyperspectral video benchmarks demonstrate that our method achieves competitive performance against state-of-the-art hyperspectral trackers, especially in scenarios with background clutter.