NDLI: Compressed hyperspectral imagery for forestry

Content Provider	IEEE Xplore Digital Library
Author	Dyk, A. Goodenough, D.G. Thompson, S. Nadeau, C. Hollinger, A. Shen-En Qian
Copyright Year	2003
Description	Author affiliation: Pacific Forestry Centre, Natural Resources Canada, Victoria, BC, Canada (Dyk, A.; Goodenough, D.G.)
Abstract	Various compression schemes have been suggested for storage and distribution of hyperspectral remotely sensed data. Hyperspectral forestry applications that rely on the measurement of subtle variations in the spectral signature of the forest canopy can be affected by modification of the spectra induced by compression. As part of an experiment for the Canadian Space Agency (CSA), Hyperion data cubes acquired over the Greater Victoria Watershed District (GVWD) were compressed using the Successive Approximation Multi-stage Vector Quantization (SAMVQ) and Hierarchical Self-Organizing Cluster Vector Quantization (HSOCVQ) algorithms developed by CSA. The data were compressed using compression ratios 10:1 and 20:1 and were returned uncompressed. The data cubes were classified into forest species using the same supervised classification methodology as applied to the original data. The classification accuracies were compared. For some applications, one can achieve significant reductions in data volume through compression. Of the compression algorithms and ratios tested, SAMVQ 10:1 has the least overall effect but still reduces classification accuracies on difficult to separate classes. While uncompressed data are preferred, SAMVQ 10:1 compression may be suitable for forest inventory.
Starting Page	294
Ending Page	296
File Size	1359270
Page Count	3
File Format	PDF
ISBN	0780379292
DOI	10.1109/IGARSS.2003.1293754
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-21
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Hyperspectral imaging Forestry Hyperspectral sensors Vector quantization Clustering algorithms Computer science Application software Compression algorithms Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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