An Algorithm of Improved Optimum Index Factor Band Selection from Hyperspectral Remote Sensing Image
| Autor: | Ting-yan Xing, Li-shuan Hu, Qun Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Computer science 0211 other engineering and technologies Hyperspectral imaging Feature selection 02 engineering and technology 01 natural sciences Field (computer science) Image (mathematics) Support vector machine Discriminative model Remote sensing (archaeology) Factor (programming language) computer 010606 plant biology & botany 021101 geological & geomatics engineering Remote sensing computer.programming_language |
| Zdroj: | DEStech Transactions on Computer Science and Engineering. |
| ISSN: | 2475-8841 |
| Popis: | Hyperspectral remote sensing sensors can capture hundreds of narrow contiguous bands and provide plenty of valuable information. Due to the high-dimension characteristics of hyperspectral data, band selection plays an important role in the field of Hyperspectral Image (HSI) classification. In this paper, a HSI classification method based on Improved Optimum Index Factor (IOIF) band selection is proposed. First, we review the standard OIF and its shortcoming for processing huge bands. Then we introduce the proposed feature selection method based on IOIF. Experiments are conducted on the Indian Pines dataset. The evaluation results show that the proposed approach can select those bands with more discriminative information rapidly and improve the classification accuracy effectively. |
| Databáze: | OpenAIRE |
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