Information Retrieval With Chessboard-Shaped Topology for Hyperspectral Target Detection

Given a priori knowledge, hyperspectral target detection aims to locate objects of interest within specific scenes by utilizing differences in spectral characteristics among various land covers. However, for those traditional model-driven detectors with monotonic analytical mode, they perform mediocrely in the disassembly of hyperspectral image (HSI) data, failing to cope with real scenes with complexity. The discrepancy between fixed model assumptions and HSI data severely reduces detection effects, leading to the inability of such methods to mine deep-level features and adapt to the variability of imaging scenes. To overcome the limitations of traditional methods, we propose a chessboard-shaped topological framework for high-dimensional data structures to disassemble an HSI from both spatial and spectral dimensions adaptively. With hyperspectral target detection refined into an information retrieval task in a topological space, a chessboard-shaped topology for hyperspectral target detection (CTTD) is proposed. In the topological space, latent and hidden data features of original images are presented in an intuitive way. Therefore, the differences in both spatial and spectral dimensions between the two classes of objects, namely, target and background, are specifically amplified and exploited to perform the information retrieval task with superior performance. Extensive experimental results on benchmark HSI datasets demonstrate that CTTD can efficiently adapt to the variability of real scenes while extracting abundant and detailed information for accurate target localization. Moreover, both detection effects and computational efficiency exhibited by the proposed method provide strong support for its popularization in practical applications.