Hyperspectral (HS) images hold both spatial and spectral information of an imaged scene. This allows one to take advantage of the distinct spectral signatures of materials to perform classification tasks. Since HS data are also typically very large and redundant, it is appealing to utilize compressive sensing (CS) techniques for HS acquisition. CS avoids the need for postacquisition digital compression, as the compression is inherently performed electrooptically during acquisition. We research the performance of deep learning classification applied directly on the compressive measurements. We show that by using a spectral CS technique we previously developed, it is possible to reduce the captured data by an order of magnitude without significant loss in the classification performance.

中文翻译：

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压缩感知光谱图像域中的深度神经网络分类

高光谱 (HS) 图像包含成像场景的空间和光谱信息。这允许人们利用材料的不同光谱特征来执行分类任务。由于 HS 数据通常也非常大且冗余，因此利用压缩传感 (CS) 技术进行 HS 采集很有吸引力。CS 避免了对采集后数字压缩的需要，因为在采集过程中压缩本质上是通过光电方式进行的。我们研究了直接应用于压缩测量的深度学习分类的性能。我们表明，通过使用我们之前开发的光谱 CS 技术，可以将捕获的数据减少一个数量级，而不会显着降低分类性能。