Zhang Hao, Ma Xu, Zhao Xianhong, Arce Gonzalo R
Opt Express. 2021 Oct 11;29(21):32875-32891. doi: 10.1364/OE.437717.
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.
高光谱图像分类（HIC）是遥感领域一个活跃的研究课题。高光谱图像通常会生成大型数据立方体，在数据采集、存储、传输和处理方面带来巨大挑战。为克服这些限制，本文基于编码孔径快照光谱成像仪（CASSI）的压缩测量开发了一种新颖的深度学习HIC方法，无需重建完整的高光谱数据立方体。提出了一种新型深度学习策略，即3D编码卷积神经网络（3D-CCNN），以有效解决分类问题，其中基于硬件的编码孔径被视为逐像素连接的网络层。开发了一种端到端训练方法，以联合优化网络参数和具有周期性结构的编码孔径。通过利用深度学习网络与编码孔径之间的协同作用，有效提高了分类精度。在多个高光谱数据集上，将所提方法的优越性与现有最先进的HIC方法进行了评估。
