Hyperspectral imaging payloads are widely employed in diverse applications, including geological surveys, plant research, resource exploration, and more. Despite the emergence of spaceborne hyperspectral imaging, airborne imagers remain crucial in remote sensing due to their superior performance capabilities. This article offers a comprehensive review of hyperspectral imaging payload development and application across various platforms and instruments, with a specific focus on the limitations of unmanned aerial vehicle-borne platforms. Ultimately, this research points out there are several challenges that need to be addressed in future studies and presents an outlook of challenges and future trends. This article also assists researchers in quickly gaining an overview of the existing vast literature in the related fields.
Object detection from hyperspectral images (HSIs) is an important issue but encounters a critical challenge that results in poor detection due to the variation of the detection object spectrum. Especially when the detection object area is large and widely distributed in HSIs, such spectral variability becomes more serious. Spectral variability can make false detection and leak detection in object detection very serious. The constrained energy minimization (CEM) algorithm is a classical object detection algorithm that only needs the object prior spectrum to achieve object detection, but the spectral variability will have a detrimental effect on the detection results of the CEM algorithm. To address the above problems, we propose a multiobject subspace projection sample weighted CEM (MSPSW-CEM) algorithm. The proposed method has the following capabilities: (1) it constructs object subspaces and detectors using multiple prior spectra of the detection object under spectral variability conditions and (2) it utilizes the subspace projection theory to weight the pixel spectra, so that the detector can better suppress the background information and highlight the object information. Extensive experiments were carried out on two sets of real-world HSIs, and it was found that MSPSW-CEM generally showed a better detection performance than other object detection methods.
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