Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation.

中文翻译：

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深度学习密集植被区人员和车辆的高光谱图像自动标注和光谱信号分类

尽管最近在图像和视频处理方面取得了进步，但是由于景观，照明变化和强烈的遮挡，在茂密植被中检测人或汽车仍然具有挑战性。在本文中，我们通过使用安装在地面或可能安装在无人机上的高光谱摄像机以及基于光谱特征的检测来解决此问题。我们结合最先进的深度学习方法，推出了一种新颖的自动注释光谱特征的方法。用我们的方法收集了数百万个样本后，我们使用了深度学习方法来训练分类器以检测人和汽车。我们的结果表明，仅基于频谱特征分类，我们可以实现0.83的Matthews相关系数。我们评估了植被变化地区的分类方法，并讨论了当前高光谱成像技术所具有的局限性和局限性。我们得出结论，在不受控制的室外环境中，光谱签名分类是可能的，并且具有很高的准确性。然而，即使采用最先进的紧凑型被动高光谱成像技术，在为茂密植被区域开发物体检测算法时，高动态范围照明和相对较低的图像分辨率仍构成重大挑战。