Name
Abstract | ||
|---|---|---|
Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/TGRS.2002.802494 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
feature extraction,geophysical signal processing,image classification,mathematical morphology,remote sensing,spectral analysis,spectral-domain analysis,classification,endmember extraction,ground components,image processing technique,interpretation,mathematical morphology,multidimensional morphological operations,reference signatures,reflectance spectrum,remotely sensed multidimensional imagery,spatial correlation,spatial information,spatial/spectral endmember extraction,spectral domain,spectral information,spectral mixture analysis,spectral unmixing,unsupervised pixel purity determination | Spatial analysis,Endmember,Multidimensional analysis,Remote sensing,Image processing,Artificial intelligence,Subpixel rendering,Computer vision,Pattern recognition,Hyperspectral imaging,Feature extraction,Pixel,Mathematics | Journal |
Volume | Issue | ISSN |
40 | 9 | 0196-2892 |
Citations | PageRank | References |
192 | 19.63 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Plaza | 1 | 3475 | 262.63 |
| pablo martinez | 2 | 617 | 58.77 |
| Rosa Pérez | 3 | 443 | 45.46 |
| Javier Plaza | 4 | 561 | 58.04 |