End-to-End Compressive Spectral Classification: A Deep Learning Approach Applied to the Grading of Tahiti Lime

Abstract

Compressed sensing (CS) theory enables the reconstruction of spectral images (SI) using a lower number of measurements than the traditional Shannon-Nyquist sampling approach, through compressive spectral imaging (CSI) systems. These CSI systems rely on a dispersive-based optical setup coupled to one or more coded-apertures to capture and compress a spectral scene simultaneously. Afterward, the reconstruction of the underlying scene is obtained through computational algorithms. Then, processing tasks like classification, object detection, or segmentation are performed over the reconstructed images. However, this reconstruction process is computationally expensive, which introduces a time overhead for these tasks. In this paper, spectral classification is directly performed over compressed measurements acquired through an optical architecture following the CS framework. An end-to-end method to optimize both coded-apertures and deep learning model parameters is proposed. This approach has been applied to the grading of Tahiti lime (Citrus latifolia), but can be used for different agricultural materials. In this specific case, the classification accuracy reached 99%. In addition, for the purpose of comparison, our experiments improved up to 7% in classification accuracy over a testing database when the coded-apertures were optimized.