Cross-satellite hierarchical multimodal denoising for hyperspectral imagery

Abstract

The rapid proliferation of remote sensing satellite constellations has ushered in an era of unprecedented access to hyperspectral (HS) and multispectral (MS) Earth observation data. HS denoising remains a critical research focus for enhancing the interpretation and application of satellite data. However, on the one hand, the available characteristics of noisy HS satellite data are often limited under real-world and unknown conditions. On the other hand, existing works ignore the complementary advantages offered by MS satellite imagery that can be employed to enhance HS denoising performance. Leveraging the synergistic potential of HS and MS data, this study pioneers a novel paradigm for the joint exploitation and optimization of multi-source remote sensing satellites. A novel Hierarchical Dual Tucker Decomposition (DTucker) framework is proposed to capitalize on the low-rank (LR) tensor property of HS and MS cross-satellite data. The inheritance of properties from the original tensor to the core tensor is explored through a manually designed model-driven constraint or a data-driven multilayer perceptron (MLP) framework. This enables robust integration of MS-derived spatial richness with HS details and significantly enhances the denoising capability. We construct HS–MS data pairs from real satellite observations, including Earth Observing-1, Sentinel-2, Gaofen-1, Gaofen-5, and Gaofen-6. Four datasets span diverse scenes such as runways, urban areas, rivers, and farmlands. The proposed method demonstrates strong generalization across various satellite combinations and application scenarios. Notably, the incorporation of MS data markedly enhances both class discrimination and structural detail in the denoised HS outputs, promoting the performance of subsequent classification and analysis tasks. The datasets and codes implemented in MATLAB and PYTHON will be available on the website: https://github.com/MinghuaWang123/DTucker, contributing to the remote sensing community.