Name
Title | ||
|---|---|---|
Morphology-based structure-preserving projection for spectral–spatial feature extraction and classification of hyperspectral data |
Abstract | ||
|---|---|---|
Incorporation of spatial information besides rich spectral information of hyperspectral image significantly enhances data classification accuracy. A morphology-based feature extraction and classification framework is proposed here, which includes the local neighbourhood information in a spatial window for extension of training set. The proposed method is morphology-based structure-preserving projection (MSPP) and tries to preserve the data structure in spectral–spatial feature space. Moreover, MSPP increases the class discrimination ability by defining a similarity matrix constructed by extended spectral–spatial training samples. The experimental results show the superiority of MSPP compared to some state-of-the-art classification methods from the classification accuracy point of view. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1049/iet-ipr.2017.1431 | IET Image Processing |
Keywords | Field | DocType |
image classification,feature extraction,data structures,hyperspectral imaging,matrix algebra | Spatial analysis,Data structure,Computer vision,Feature vector,Pattern recognition,Hyperspectral imaging,Feature extraction,Neighbourhood (mathematics),Artificial intelligence,Data classification,Class discrimination,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 2 | 1751-9659 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maryam Imani | 1 | 61 | 8.65 |
| Hassan Ghassemian | 2 | 396 | 34.04 |