Segmented PCA-bsed Compression for Hyperspectral Image Analysis

Hyperspectral images have high spectral resolution that helps to improve object classification. But its vast data volume also causes problems in data transmission and data storage. Since there is high correlation among spectral bands in a hyperspectral image, how to reduce the data redundancy while keeping the important information for the following data analysis is a challenging task. In this paper, we investigate a compression technique based on segmented Principal Components Analysis (PCA). A hyperspectral image cube is divided into several non-overlapping blocks in accordance with band-to-band cross-correlations, followed by the PCA performed in each block. A major advantage resulting from this approach is computational efficiency. The utility of the proposed segmented PCA-based compression in target detection and classification will be investigated. The experiments demonstrate that the segmented PCA-based compression generally outperforms PCA-based compression in terms of higher detection and classification accuracy on decompressed hyperspectral image data.