Name
Title | ||
|---|---|---|
Multiscale Feature Extraction with Gaussian Curvature Filter for Hyperspectral Image Classification |
Abstract | ||
|---|---|---|
In this paper, in order to extract efficient spectral-spatial features for hyperspectral image classification, a Gaussian curvature (GC) filter based feature extraction method with multiscale segmentation constraint is proposed. The method consists of the following major steps: First, the maximum noise fraction (MNF) method is applied on the hyperspectral images (HSIs) to reduce the noise and computational complexity. The GC features are extracted from the dimension reduced HSIs via the GC filter. Next, a multiscale segmentation strategy is applied on the HSIs, and the multiscale spatial features are extracted by applying the weighted mean operations within and among superpixels. Finally, the GC features and dimension reduced multiscale spatial features are fused to form the final multiscale Gaussian curvature features (MGCFs) for classification purposes. To verify the effectiveness of the proposed method, we conduct experiments on the Indian Pines data set. Experimental results demonstrate that the proposed method can significantly improve the classification accuracies compared to several standard classification methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9323640 | IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium |
Keywords | DocType | ISSN |
Hyperspectral image classification,multiscale feature extraction,curvature filter,image segmentation | Conference | 2153-6996 |
ISBN | Citations | PageRank |
978-1-7281-6375-8 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiaobo Hao | 1 | 1 | 3.05 |
| Shutao Li | 2 | 2594 | 139.10 |
| Leyuan Fang | 3 | 639 | 33.52 |
| Xudong Kang | 4 | 451 | 22.68 |