The Computed Tomography Imaging Spectrometer (CTIS) permits a snapshot acquisition of a hyperspectral cube, through the creation of an image of indirect measurements which is then traditionally used for reconstruction of the cube. This reconstruction step is time-consuming and only yields an approximation of the original cube. Following a compressed learning framework, we compare the performance of a classification task carried out on reconstructed cubes on one hand, directly on the raw images on the other. Regarding the latter case, we propose in particular the use of a new Convolutional Neural Network (CNN) architecture called CTIS-Net, whose architecture is tailored to benefit from the specific structure of CTIS images. Results show a sizable increase compared to classification with a standard architecture and compared to a conventional classification on the reconstructed cubes.

中文翻译：

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CTIS-Net：基于计算机断层扫描成像光谱仪的压缩学习神经网络架构


计算机断层扫描成像光谱仪 (CTIS) 通过创建间接测量的图像，允许对高光谱立方体进行快照采集，然后传统上将其用于重建立方体。此重建步骤非常耗时，并且只能产生原始立方体的近似值。按照压缩学习框架，我们一方面比较在重建立方体上执行的分类任务的性能，另一方面直接在原始图像上进行分类任务的性能。对于后一种情况，我们特别建议使用一种称为 CTIS-Net 的新卷积神经网络 (CNN) 架构，该架构经过定制以受益于 CTIS 图像的特定结构。结果显示，与使用标准架构的分类以及与重建立方体上的传统分类相比，有相当大的提高。