Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis
Photogrammetric Engineering and Remote Sensing
By: P.S. Chavez Jr. and Andy Y. Kwarteng
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Abstract
A challenge encountered with Landsat Thematic Mapper (TM) data, which includes data from size reflective spectral bands, is displaying as much information as possible in a three-image set for color compositing or digital analysis. Principal component analysis (PCA) applied to the six TM bands simultaneously is often used to address this problem. However, two problems that can be encountered using the PCA method are that information of interest might be mathematically mapped to one of the unused components and that a color composite can be difficult to interpret. "Selective' PCA can be used to minimize both of these problems. The spectral contrast among several spectral regions was mapped for a northern Arizona site using Landsat TM data. Field investigations determined that most of the spectral contrast seen in this area was due to one of the following: the amount of iron and hematite in the soils and rocks, vegetation differences, standing and running water, or the presence of gypsum, which has a higher moisture retention capability than do the surrounding soils and rocks. -from Authors
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis |
| Series title | Photogrammetric Engineering and Remote Sensing |
| Volume | 55 |
| Issue | 3 |
| Year Published | 1989 |
| Language | English |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Photogrammetric Engineering and Remote Sensing |
| First page | 339 |
| Last page | 348 |
| Google Analytic Metrics | Metrics page |