Hyperspectral target detection (HTD) aims to identify specific materials based on spectral information in hyperspectral imagery and can detect point targets, some of which occupy a smaller than one-pixel area. However, existing HTD methods are developed based on per-pixel binary classification, which limits the feature representation capability for point targets. In this paper, we rethink the hyperspectral point target detection from the object detection perspective, and focus more on the object-level prediction capability rather than the pixel classification capability. Inspired by the token-based processing flow of Detection Transformer (DETR), we propose the first specialized network for hyperspectral multi-class point object detection, SpecDETR. Without the backbone part of the current object detection framework, SpecDETR treats the spectral features of each pixel in hyperspectral images as a token and utilizes a multi-layer Transformer encoder with local and global coordination attention modules to extract deep spatial-spectral joint features. SpecDETR regards point object detection as a one-to-many set prediction problem, thereby achieving a concise and efficient DETR decoder that surpasses the current state-of-the-art DETR decoder in terms of parameters and accuracy in point object detection. We develop a simulated hyperSpectral Point Object Detection benchmark termed SPOD, and for the first time, evaluate and compare the performance of current object detection networks and HTD methods on hyperspectral multi-class point object detection. SpecDETR demonstrates superior performance as compared to current object detection networks and HTD methods on the SPOD dataset. Additionally, we validate on a public HTD dataset that by using data simulation instead of manual annotation, SpecDETR can detect real-world single-spectral point objects directly.

翻译：高光谱目标检测旨在根据高光谱图像中的光谱信息识别特定材料，并能检测到面积小于一个像素的点目标。然而，现有高光谱目标检测方法基于逐像素二分类开发，这限制了点目标的特征表示能力。本文从目标检测角度重新思考高光谱点目标检测，更关注目标级预测能力而非像素分类能力。受检测Transformer（DETR）基于令牌的处理流程启发，我们提出了首个专门用于高光谱多类点目标检测的网络——SpecDETR。SpecDETR摒弃现有目标检测框架的主干网络部分，将高光谱图像中每个像素的光谱特征视为一个令牌，并利用包含局部与全局协调注意力模块的多层Transformer编码器提取深度空间-光谱联合特征。SpecDETR将点目标检测视为一对多集合预测问题，从而构建简洁高效的DETR解码器，该解码器在点目标检测中的参数规模和精度均超越当前最先进的DETR解码器。我们开发了名为SPOD的模拟高光谱点目标检测基准，首次评估并比较了现有目标检测网络与高光谱目标检测方法在高光谱多类点目标检测中的性能。在SPOD数据集上，SpecDETR展现出优于当前目标检测网络和高光谱目标检测方法的性能。此外，我们在公开高光谱目标检测数据集上验证，通过使用数据模拟而非人工标注，SpecDETR可直接检测真实单光谱点目标。