A novel adaptive compression method for hyperspectral images by using EDT and particle swarm optimization

Pedram Ghamisi; Lalit Kumar

doi:10.1117/12.904727

24 January 2012 A novel adaptive compression method for hyperspectral images by using EDT and particle swarm optimization

Pedram Ghamisi, Lalit Kumar

Author Affiliations +

Proceedings Volume 8299, Digital Photography VIII; 82990M (2012) https://doi.org/10.1117/12.904727
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

Hyperspectral sensors generate useful information about climate and the earth surface in numerous contiguous narrow spectral bands, and are widely used in resource management, agriculture, environmental monitoring, etc. Compression of the hyperspectral data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as hyperspectral data. Due to high redundancy in neighboring spectral bands and the tendency to achieve a higher compression ratio, using adaptive coding methods for hyperspectral data seems suitable for this purpose. This paper introduces two new compression methods. One of these methods is adaptive and powerful for the compression of hyperspectral data, which is based on separating the bands with different specifications by the histogram and Binary Particle Swarm Optimization (BPSO) and compressing each one a different manner. The new proposed methods improve the compression ratio of the JPEG standards and save storage space the transmission. The proposed methods are applied on different test cases, and the results are evaluated and compared with some other compression methods, such as lossless JPEG and JPEG2000.

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Pedram Ghamisi and Lalit Kumar "A novel adaptive compression method for hyperspectral images by using EDT and particle swarm optimization", Proc. SPIE 8299, Digital Photography VIII, 82990M (24 January 2012); https://doi.org/10.1117/12.904727

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KEYWORDS

Image compression

Particles

Signal to noise ratio

JPEG2000

Visualization

Particle swarm optimization

Visual compression

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