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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Heinz, D.C. Chein-I Chang |
| Copyright Year | 2000 |
| Description | Author affiliation: Remote Sensing Signal & Image Process. Lab., Maryland Univ., Baltimore, MD, USA (Heinz, D.C.) |
| Abstract | Subpixel detection and quantification of materials in multispectral imagery presents a challenging problem due to a relatively low number of spectral bands available for analysis in which case the number of spectral bands may be less than the number of materials to be detected and quantified. The problem is even more difficult when the image scene is unknown and no prior knowledge is available. Under this circumstance, the desired information must be obtained directly from the image data. The authors present an unsupervised least squares-based linear mixture analysis method coupled with a band expansion technique for multispectral image analysis. This method allows the authors to extract necessary endmember information from an unknown image scene so that the endmembers present in the image can be detected and quantified. The band expansion technique creates additional bands from the existing multispectral bands using band-to-band nonlinear correlation. These expanded bands ease the problem of insufficient bands in multispectral imagery and can improve and enhance the performance of the proposed method. The experimental results demonstrate the advantages of the proposed approach. |
| Starting Page | 1681 |
| Ending Page | 1683 |
| File Size | 334896 |
| Page Count | 3 |
| File Format | |
| ISBN | 0780363590 |
| DOI | 10.1109/IGARSS.2000.857311 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-07-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Least squares methods Spectral analysis Multispectral imaging Layout Image analysis Vectors Hyperspectral sensors Hyperspectral imaging Pixel Least squares approximation |
| Content Type | Text |
| Resource Type | Article |
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