Spectro-TwinNet: Dual-Stream Temporal Deep Learning for Robust Hyperspectral Land-Cover Classification

Spectral noise, high dimensionality, and limited time modeling in hyperspectral data render adequate prediction of Land Use and Land Cover (LULC) very difficult. To solve these problems, it applies Spectro-TwinNet, a spectral-spatial deep learning architecture that combines adaptive preprocessing and enhanced temporal representation. The proposed ANSER module considerably improves the quality of data, decreasing MSE to 0.061, raising PSNR to 61.27 dB, and SSIM to 0.944, which is better than traditional filters. To extract features and classify 3DSpectro-TwinTCN architecture is designed to combine 3D spectral encoding and dual-stream temporal learning with maximizing 97.6 % accuracy, 94.8 % sensitivity, and 95.2 % specificity, where the application of high spectral robustness and temporal consistency is provided. Experimental analyses attest to the fact that unified preprocessing and a deep temporal modeling method provide better discrimination ability of complex land-cover patterns. The findings make Spectro-TwinNet a useful tool forhigh-precision and reliable prediction of hyperspectral LULC.