Novel Data-Driven Spatial-Spectral Correlated Scheme for Dimensionality Reduction of Hyperspectral Images

Hyperspectral imaging technology has been popularly applied in remote sensing because it collects echoed signals from across the electromagnetic (EM) spectrum and thereby contributes fruitfully spatial-spectral information. However, the processing or storage of high-data-volume hyperspectral images (HSIs), also viewed as snapshots varying with the EM spectrum, burdens the hardware resources, especially for the high spectral resolution and spatial resolution cases. To address this challenge, a novel unsupervised dimensionality reduction method based on the dynamic mode decomposition (DMD) algorithm is proposed to analyze hyperspectral data. This method decomposes the spatial-spectral HSIs in terms of spatial dynamic modes and corresponding spectral patterns. Then, these spatial-spectral patterns are combined to reconstruct the raw HSIs via a low-rank model. Furthermore, we extend the proposed DMD method to hyperspectral data in the tensor form and title it CubeDMD to actualize the compression of HSIs in horizontal, vertical, and spectral dimensions. Our proposed data-driven scheme is benchmarked by the real hyperspectral data measured at the Salinas scenes and Pavia University. It is demonstrated that the HSIs can be reconstructed accurately and effectively by the proposed low-rank model. The mean peak signal-to-noise ratio between the reconstructed and original HSIs can reach 31.47 dB, and the corresponding mean spectral angle mapper is only 0.1037. Our work provides a useful tool for the analysis of HSIs with a low-rank representation.