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Linear mixture analysis-based compression for hyperspectral image analysis

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2 Author(s)
Qian Du ; Dept. of Electr. Eng. & Comput. Sci., Texas A&M Univ., Kingsville, TX, USA ; Chein-I Chang

Due to significantly improved spectral resolution produced by hyperspectral sensors, the band-to-band correlation is generally very high and can be removed without loss of crucial information. Data compression is an effective means to eliminate such redundancy resulting from high interband correlation. In hyperspectral imagery, various information comes from different signal sources, which include man-made targets, natural backgrounds, unknown clutters, interferers, unidentified anomalies, etc. In many applications, whether or not a compression technique is effective is measured by the degree of information loss rather than information recovery. For example, compression of noise or interferers is highly desirable to image analysis and interpretation. In this paper, we present an unsupervised fully constrained least squares (UFCLS) linear spectral mixture analysis (LSMA)-based compression technique for hyperspectral target detection and classification. Unlike most compression techniques, which deal directly with grayscale images, the proposed compression approach generates and encodes the fractional abundance images of targets of interest present in an image scene to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in these fractional abundance images, the loss of information may have little impact on image analysis. On some occasions, it even improves performance analysis. Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data are used for experiments to evaluate our proposed LSMA-based compression technique used for applications in hyperspectral detection and image classification. The classification results using the original data and the UFCLS-decompressed data are shown to be very close with no visible difference. But a compression ratio for the HYDICE data with water bands removed can achieve as high as 138:1 with peak SNR (PSNR) 33 dB, while a compression ratio of the AVIRIS scene also with water bands removed is 90:1 with PSNR 40 dB.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:42 ,  Issue: 4 )