Joint Collaborative Representation With Shape Adaptive Region and Locally Adaptive Dictionary for Hyperspectral Image Classification
| Autor: | Jinghui Yang, Jinxi Qian |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pixel
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Hyperspectral imaging Pattern recognition Filter (signal processing) Geotechnical Engineering and Engineering Geology Class (biology) Image (mathematics) Support vector machine Computer Science::Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Representation (mathematics) Joint (audio engineering) Spatial analysis |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters. 17:671-675 |
| ISSN: | 1558-0571 1545-598X |
| DOI: | 10.1109/lgrs.2019.2929840 |
| Popis: | A novel hyperspectral image (HSI) classification method based on joint collaborative representation with shape adaptive region and locally adaptive dictionary (SALJCR) is proposed in this letter. First, the shape adaptive (SA) region is selected for each pixel to exploit the neighboring spatial information adaptively. The average filtering (according to SA regions) is performed for the whole image. Then, based on the filtered image, a locally adaptive dictionary is constructed for each test pixel to reduce the negative impact of irrelevant pixels on representation. Finally, a joint collaborative representation method is applied to decompose the pixels and assign the class label. Experimental results demonstrate that the proposed SALJCR method outperforms some state-of-the-art classifiers. |
| Databáze: | OpenAIRE |
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