Name
Abstract | ||
|---|---|---|
In this paper, a novel multi-scale total variation method is proposed to extract structural features from hyperspectral images (HSIs), which consists of the following steps. First, the spectral dimension of the HSI is reduced with an averaging-based method. Then, the multi-scale structural features (MSFs), which are insensitive to image noise, are constructed with a relative total variation-based structure extraction technique. Finally, the MSFs are fused together using kernel principal component analysis (KPCA), so as to obtain the KPCA-fused MSFs for classification. Experimental results on three publicly available hyperspectral datasets, including both well-known, long-used data, and a recent dataset obtained from an international contest, demonstrate the competitive performance over several state-of-the-art classification approaches in this field. Moreover, the robustness of the proposed method to the small-sample-size problem and serious image noise is also demonstrated. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/jstars.2019.2915272 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Feature extraction,Hyperspectral imaging,Kernel,Principal component analysis,Noise robustness | Kernel (linear algebra),Hyperspectral image classification,Computer vision,Kernel principal component analysis,Hyperspectral imaging,Robustness (computer science),Feature extraction,Image noise,Artificial intelligence,Mathematics,Principal component analysis | Journal |
Volume | Issue | ISSN |
12 | SP6.0 | 1939-1404 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Puhong Duan | 1 | 2 | 5.44 |
| Xudong Kang | 2 | 451 | 22.68 |
| Shutao Li | 3 | 2594 | 139.10 |
| Pedram Ghamisi | 4 | 827 | 46.28 |