基于压缩感知理论的波段重构方法

针对高光谱图像数据量大、信息冗余多、传输难度大等问题，从波段压缩采样入手，通过采样数据重构出原始波段，提出一种基于压缩感知理论的波段重构方法。压缩感知理论是一种在不遵循奈奎斯特采样定理的情况下，能够高精度重构出原始信号的新型压缩采样理论。由于高光谱图像谱间相关性高，具有很强的稀疏性，故可将压缩感知理论用于高光谱数据的波段重构，仅选择少量波段，便能够重构得到原始高光谱数据。实验结果表明，压缩感知理论能够对高光谱图像波段维进行压缩与重构，并可达到较高的重构比例，同时获得较高的重构效率，且重构数据光谱曲线与原始数据光谱曲线的波形一致度高。

Hyperspectral band reconstruction based on compressed sensing theory

Hyperspectral image processing had attracted high attention in remote sensing fields. One of the main issues was to address the problem of huge data and hard transmission via sampling and reconstruction. Compressed sensing theory was investigated in this paper for band reconstruction. Based on compressed sensing theory, original signal could be reconstructed efficiently without satisfying the Nyquist-Shannon criterion. Adjacent spectral bands of hyperspectral images were highly correlated, resulting in strong sparse representation. This significant property made it possible to obtain the whole spectrum information from limited bands of original hyperspectral data via compressed sensing theory. Experimental results demonstrate the feasibility and reliability of applying compressed sensing theory for sampling and reconstruction on bands of hyperspectral images. The proposed band reconstruction method can perform high correlation coefficients and low relative errors between a pair of reconstructed and original hyperspectral bands. Simultaneously, high levels of reconstruction efficiency are achieved, and reconstructed spectral curve is in accordance with original data as well.