Visualization, Band Ordering and Compression of Hyperspectral Images
Air-borne and space-borne acquired hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. The state of the art compressor for lossless compression of hyperspectral images is the Spectral oriented Least SQuares (SLSQ) compressor (see [1–7]). In this...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2012-02-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1999-4893/5/1/76/ |
| _version_ | 1818440701925392384 |
|---|---|
| author | Raffaele Pizzolante Bruno Carpentieri |
| author_facet | Raffaele Pizzolante Bruno Carpentieri |
| author_sort | Raffaele Pizzolante |
| collection | DOAJ |
| description | Air-borne and space-borne acquired hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. The state of the art compressor for lossless compression of hyperspectral images is the Spectral oriented Least SQuares (SLSQ) compressor (see [1–7]). In this paper we discuss hyperspectral image compression: we show how to visualize each band of a hyperspectral image and how this visualization suggests that an appropriate band ordering can lead to improvements in the compression process. In particular, we consider two important distance measures for band ordering: Pearson’s Correlation and Bhattacharyya distance, and report on experimental results achieved by a Java-based implementation of SLSQ. |
| first_indexed | 2024-12-14T18:16:33Z |
| format | Article |
| id | doaj.art-b69ccc619d3643559d8cce9d4c73fa77 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-14T18:16:33Z |
| publishDate | 2012-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-b69ccc619d3643559d8cce9d4c73fa772022-12-21T22:52:10ZengMDPI AGAlgorithms1999-48932012-02-0151769710.3390/a5010076Visualization, Band Ordering and Compression of Hyperspectral ImagesRaffaele PizzolanteBruno CarpentieriAir-borne and space-borne acquired hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. The state of the art compressor for lossless compression of hyperspectral images is the Spectral oriented Least SQuares (SLSQ) compressor (see [1–7]). In this paper we discuss hyperspectral image compression: we show how to visualize each band of a hyperspectral image and how this visualization suggests that an appropriate band ordering can lead to improvements in the compression process. In particular, we consider two important distance measures for band ordering: Pearson’s Correlation and Bhattacharyya distance, and report on experimental results achieved by a Java-based implementation of SLSQ.http://www.mdpi.com/1999-4893/5/1/76/lossless compressionimage compressionhyperspectral imagesband orderingremote sensing3D data |
| spellingShingle | Raffaele Pizzolante Bruno Carpentieri Visualization, Band Ordering and Compression of Hyperspectral Images Algorithms lossless compression image compression hyperspectral images band ordering remote sensing 3D data |
| title | Visualization, Band Ordering and Compression of Hyperspectral Images |
| title_full | Visualization, Band Ordering and Compression of Hyperspectral Images |
| title_fullStr | Visualization, Band Ordering and Compression of Hyperspectral Images |
| title_full_unstemmed | Visualization, Band Ordering and Compression of Hyperspectral Images |
| title_short | Visualization, Band Ordering and Compression of Hyperspectral Images |
| title_sort | visualization band ordering and compression of hyperspectral images |
| topic | lossless compression image compression hyperspectral images band ordering remote sensing 3D data |
| url | http://www.mdpi.com/1999-4893/5/1/76/ |
| work_keys_str_mv | AT raffaelepizzolante visualizationbandorderingandcompressionofhyperspectralimages AT brunocarpentieri visualizationbandorderingandcompressionofhyperspectralimages |