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Abstract
In recent years, compressive spectral imaging (CSI) has emerged as a new acquisition technique that acquires coded projections of the spectral scene, reducing considerably storage and transmission costs. Among several CSI devices, the single-pixel camera (SPC) architecture excels due to its low implementation cost when acquiring a large number of spectral bands. Although CSI allows efficient sampling, a complete reconstruction of the underlying scene is needed to perform any processing task, which involves solving a computationally expensive optimization problem. In this paper, we propose a fast method to classify the underlying spectral image by directly using compressed SPC measurements, avoiding reconstruction. In particular, the proposed method acquires an RGB image of the scene as side information to design the SPC coding patterns. Our design approach allows incorporating the similarity information of neighboring pixels from the RGB image into compressed measurements. After acquiring the compressed measurements with our designed coding patterns, we extract features of the scene to perform classification without reconstruction. After simulations, we obtained an overall accuracy of 95.41% and 97.72% for the Pavia University and Salinas spectral images, respectively. Furthermore, we tested our approach in the laboratory and classified our own dataset, which has four different materials: flowers, sand, grass, and dry leaves, with an overall accuracy of 94.66%.
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