High spectral resolution of hyperspectral images allows the detection and classification of materials in the observed images. However, existing research on hyperspectral detection mainly focuses on pixel-level study, partially due to the low spatial resolution in typical earth observation applications. With the development of imaging techniques, high-spatial-resolution hyperspectral data can be obtained and object-level detection is necessary for many applications. In this work, the object-based hyperspectral detection problem is formulated, and a convolutional neural network is then designed based on the specific characteristics of this problem. Moreover, a hyperspectral dataset with over 400 high-quality images for object-level target detection is created. Experimental results validate the proposed framework and show its superior performance.

中文翻译：

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高光谱图像中的目标检测

高光谱图像的高光谱分辨率允许对观察到的图像中的材料进行检测和分类。但是，现有的高光谱检测研究主要集中在像素级研究，部分原因是典型的地球观测应用中空间分辨率较低。随着成像技术的发展，可以获得高空间分辨率的高光谱数据，并且在许多应用中必须进行对象级检测。在这项工作中，提出了基于对象的高光谱检测问题，然后根据该问题的具体特征设计了卷积神经网络。此外，还创建了具有400多个高质量图像的高光谱数据集，用于对象级目标检测。实验结果验证了所提出的框架并显示了其优越的性能。