论文标题
深层编码模式设计，用于压缩近红外光谱分类
Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification
论文作者
论文摘要
压缩光谱成像（CSI）已成为一种有吸引力的压缩和传感技术，主要是为了感知频谱区域，在这些区域中，传统系统导致高昂的成本（例如在近红外光谱中）。最近，考虑到嵌入在测量中的光谱信息的量，跳过重建步骤，可以直接在压缩域中执行光谱分类。因此，分类质量直接取决于传感步骤中采用的编码模式集。因此，这项工作提出了一种端到端方法，用于共同设计CSI和网络参数中使用的编码模式，以直接从嵌入的近红外压缩测量中执行光谱分类。对三维编码的光圈快照光谱成像（3D-CASSI）系统进行了广泛的仿真验证，该系统的设计优于传统和随机设计的分类精度的10％。
Compressive spectral imaging (CSI) has emerged as an attractive compression and sensing technique, primarily to sense spectral regions where traditional systems result in highly costly such as in the near-infrared spectrum. Recently, it has been shown that spectral classification can be performed directly in the compressive domain, considering the amount of spectral information embedded in the measurements, skipping the reconstruction step. Consequently, the classification quality directly depends on the set of coding patterns employed in the sensing step. Therefore, this work proposes an end-to-end approach to jointly design the coding patterns used in CSI and the network parameters to perform spectral classification directly from the embedded near-infrared compressive measurements. Extensive simulation on the three-dimensional coded aperture snapshot spectral imaging (3D-CASSI) system validates that the proposed design outperforms traditional and random design in up to 10% of classification accuracy.
