Name
Title | ||
|---|---|---|
Enhancing Hyperspectral Endmember Extraction Using Clustering and Oversegmentation-Based Preprocessing. |
Abstract | ||
|---|---|---|
Spectral mixture analysis (SMA) is an effective tool in recognition of unique spectral signatures of materials called endmembers and estimating their percentage of existence (abundance fractions). Most approaches designed in endmember extraction process are established by applying the spectral information of the dataset and, thus, tend to neglect the existing spatial correlation between adjacent p... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/JSTARS.2016.2539286 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Hyperspectral imaging,Clustering algorithms,Algorithm design and analysis,Indexes,Correlation | Spectral purity,Endmember,Remote sensing,Artificial intelligence,Cluster analysis,Computer vision,Full spectral imaging,Spatial correlation,Pattern recognition,Hyperspectral imaging,Pixel,Spectral signature,Mathematics | Journal |
Volume | Issue | ISSN |
9 | 6 | 1939-1404 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatemeh Kowkabi | 1 | 7 | 1.80 |
| Hassan Ghassemian | 2 | 396 | 34.04 |
| ahmad keshavarz | 3 | 22 | 5.84 |