Connecting Low-Level and High-Level Visions: A Joint Optimization for Hyperspectral Image Super-Resolution and Target Detection

Typical high-level vision tasks in hyperspectral image (HSI) processing, such as target detection, often suffer from insufficient information inherent in real-world sampled data. Super-resolution, a powerful tool in HSI low-level vision, is expected to enhance the accuracy of detection results by computationally providing the high-resolution HSI (HR-HSI) with additional information. However, existing solutions for HSI super-resolution and target detection have always been implemented independently. This conventionally adopted paradigm overlooks the interconnectedness between low-level and high-level visions, inevitably introducing additional errors, redundancies, and inefficiencies. To address this challenge, in this study, we put our efforts into exploring the uncharted continent of hyperspectral remote sensing, that is, realizing the mutual guidance and joint optimization of HSI super-resolution and target detection concurrently within a unified framework. Technically, we first construct different spectral bases to span the target and background subspaces of the underlying HR-HSI. Then, we look in-depth at the intrinsic properties of the HSI tensor, henceforth jointly optimizing both tasks by innovatively developing a novel low-cubic-rank tensor approximation model with a unique constrained energy minimization (CEM) loss. While we have developed efficient algorithms to optimize the proposed model, we also put into place a refinement procedure for spectral bases, aimed at further enhancing the spectral fidelity of the fused results and the compact representation of the target subspace. Finally, empirical studies conducted on synthetic and real-world datasets substantiate that compared with state-of-the-art solutions, the proposed method delivers highly competitive and practical performance in terms of both tasks. Source codes are available at
https://github.com/CX-He/HySRTD.git
.