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Spectral-spatial classification of hyperspectral images using hierarchical optimization
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Tilton, James C. Tarabalka, Yuliya |
| Copyright Year | 2011 |
| Description | A new spectral-spatial method for hyperspectral data classification is proposed. For a given hyperspectral image, probabilistic pixelwise classification is first applied. Then, hierarchical step-wise optimization algorithm is performed, by iteratively merging neighboring regions with the smallest Dissimilarity Criterion (DC) and recomputing class labels for new regions. The DC is computed by comparing region mean vectors, class labels and a number of pixels in the two regions under consideration. The algorithm is converged when all the pixels get involved in the region merging procedure. Experimental results are presented on two remote sensing hyperspectral images acquired by the AVIRIS and ROSIS sensors. The proposed approach improves classification accuracies and provides maps with more homogeneous regions, when compared to previously proposed classification techniques. |
| File Size | 209018 |
| Page Count | 4 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20110011233 |
| Archival Resource Key | ark:/13960/t2j72f84c |
| Language | English |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Earth Resources And Remote Sensing Airborne Equipment Spectrum Analysis Iteration Spectra Criteria Remote Sensing Pixels Image Classification Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
