Name
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 to 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 SAMVQ and 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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/IGARSS.2003.1293754 | IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES |
Keywords | DocType | ISSN |
hyperspectral, near-lossless compression, EO-1, classification forestry, Hyperion, vector quantization | Conference | 2153-6996 |
Citations | PageRank | References |
2 | 0.51 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| a dyk | 1 | 85 | 18.72 |
| David G. Goodenough | 2 | 84 | 19.70 |
| Suzanne Thompson | 3 | 12 | 1.54 |
| christian nadeau | 4 | 2 | 0.51 |
| A. B. Hollinger | 5 | 21 | 6.27 |
| Shen-En Qian | 6 | 237 | 16.96 |