Object Detection in Hyperspectral Image via Unified Spectral–Spatial Feature Aggregation

Deep learning-based hyperspectral image (HSI) classification and object detection techniques have gained significant attention due to their vital role in image content analysis, interpretation, and broader HSI applications. However, current hyperspectral object detection approaches predominantly emphasize spectral or spatial information, overlooking the valuable complementary relationship between these two aspects. In this study, we present a novel spectral–spatial aggregation (S2ADet) object detector that effectively harnesses the rich spectral and spatial complementary information inherent in the HSI. S2ADet comprises a hyperspectral information decoupling (HID) module, a two-stream feature extraction network, and a one-stage detection head. The HID module processes hyperspectral data by aggregating spectral and spatial information via band selection and principal components analysis, consequently reducing redundancy. Based on the acquired spectral and spatial aggregation information, we propose a feature aggregation two-stream network for interacting spectral–spatial features. Furthermore, to address the limitations of existing databases, we annotate an extensive dataset, designated as HOD3K, containing 3242 HSIs captured across diverse real-world scenes, and encompassing three object classes. These images possess a resolution of
$512\,\, {}\times {}256$
pixels and cover 16 bands ranging from 470 to 620 nm. Comprehensive experiments on two datasets demonstrate that S2ADet surpasses existing state-of-the-art methods, achieving robust, and reliable results. The demo code and dataset of this work are publicly available at
https://github.com/hexiao-cs/S2ADet
.