Name
Abstract | ||
|---|---|---|
Traditional joint sparse representation based hyperspectral classification methods define a local region for each pixel. Through representing the pixels within the local region simultaneously, the class of the central pixel is able to be decided. A common limitation of this kind of methods is that only local pixels are considered in such methods, and thus, non-local information will be ignored. In order to improve the classification accuracy with the non-local information of hyperspectral images, a novel hyperspectral image classification based on K nearest neighbors (KNN) sparse representation is proposed in this paper. First, a feature space is defined based on the first principal components of the hyperspectral image and the spatial coordinates of different pixels. Then, in the defined feature space, K non-local neighborhoods of each pixel are found by using the KNN searching scheme. At last, through jointly representing the K pixels with the joint sparse model and comparing the representation residuals, the label of each pixel can be determined. Experiments performed on a widely used real HSI data set show that the proposed method obtain better classification performances when compared with the traditional joint sparse representation method and other recently proposed hyperspectral image classification methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729622 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Classification, hyperspectral images, K Nearest Neighbor(KNN), joint sparse representation | k-nearest neighbors algorithm,Computer vision,Feature vector,Pattern recognition,Spatial reference system,Computer science,Sparse approximation,Hyperspectral imaging,Artificial intelligence,Pixel,Sparse matrix,Principal component analysis | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.36 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Song | 1 | 1 | 0.36 |
| Shutao Li | 2 | 2594 | 139.10 |
| Xudong Kang | 3 | 451 | 22.68 |
| Kunshan Huang | 4 | 1 | 0.36 |