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https://hdl.handle.net/1959.11/9896
Title: | A Novel Adaptive Compression Method for Hyperspectral Images by using EDT and Particle Swarm Optimization | Contributor(s): | Ghamisi, Pedram (author); Kumar, Lalit (author) | Publication Date: | 2012 | DOI: | 10.1117/12.904727 | Handle Link: | https://hdl.handle.net/1959.11/9896 | 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. | Publication Type: | Conference Publication | Conference Details: | IS&T/SPIE 2012: Information Systems & Technology and SPIE 2012 Electronic Imaging Symposium, California, United States of America, 22nd - 26th January, 2012 | Source of Publication: | Digital Photography VIII, p. 82990M-1-82990M-12 | Publisher: | International Society for Optical Engineering (SPIE) | Place of Publication: | United States of America | Fields of Research (FoR) 2008: | 090905 Photogrammetry and Remote Sensing | Fields of Research (FoR) 2020: | 401304 Photogrammetry and remote sensing | Socio-Economic Objective (SEO) 2008: | 890202 Application Tools and System Utilities | Socio-Economic Objective (SEO) 2020: | 220499 Information systems, technologies and services not elsewhere classified | Peer Reviewed: | Yes | HERDC Category Description: | E1 Refereed Scholarly Conference Publication | Series Name: | Proceedings of SPIE | Series Number : | 8299 |
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Appears in Collections: | Conference Publication |
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