Compressive spectral image classification using 3D coded convolutional neural network

TL;DR

This paper introduces a novel deep learning approach using 3D coded convolutional neural networks for hyperspectral image classification directly from compressive measurements, avoiding full data cube reconstruction.

Contribution

It proposes a new 3D coded CNN architecture that jointly optimizes network parameters and hardware coding for improved classification accuracy.

Findings

Outperforms state-of-the-art hyperspectral classification methods.

Effectively integrates hardware coding with deep learning for better results.

Reduces data processing complexity by avoiding full hyperspectral data reconstruction.

Abstract

Hyperspectral image classification (HIC) is an active research topic in remote sensing. Hyperspectral images typically generate large data cubes posing big challenges in data acquisition, storage, transmission and processing. To overcome these limitations, this paper develops a novel deep learning HIC approach based on compressive measurements of coded-aperture snapshot spectral imagers (CASSI), without reconstructing the complete hyperspectral data cube. A new kind of deep learning strategy, namely 3D coded convolutional neural network (3D-CCNN) is proposed to efficiently solve for the classification problem, where the hardware-based coded aperture is regarded as a pixel-wise connected network layer. An end-to-end training method is developed to jointly optimize the network parameters and the coded apertures with periodic structures. The accuracy of classification is effectively improved by exploiting the synergy between the deep learning network and coded apertures. The superiority of the proposed method is assessed over the state-of-the-art HIC methods on several hyperspectral datasets.