  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 wider HSI applications. However,
current hyperspectral object detection approaches predominantly emphasize
either spectral or spatial information, overlooking the valuable complementary
relationship between these two aspects. In this study, we present a novel
\textbf{S}pectral-\textbf{S}patial \textbf{A}ggregation (S2ADet) object
detector that effectively harnesses the rich spectral and spatial complementary
information inherent in hyperspectral images. 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 images
by aggregating spectral and spatial information via band selection and
principal components analysis, consequently reducing redundancy. Based on the
acquired spatial and spectral 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 3,242 hyperspectral images
captured across diverse real-world scenes and encompassing three object
classes. These images possess a resolution of 512x256 pixels and cover 16 bands
ranging from 470 nm 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 \url{https://github.com/hexiao-cs/S2ADet}.
