Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification
| Autor: | Yidong Tang, Aijun Xue, Shucai Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
K-SVD
Pixel Article Subject business.industry General Mathematics lcsh:Mathematics 0211 other engineering and technologies General Engineering Hyperspectral imaging 020206 networking & telecommunications Pattern recognition 02 engineering and technology Sparse approximation lcsh:QA1-939 Matching pursuit Multiclass classification Kernel method Kernel (image processing) lcsh:TA1-2040 0202 electrical engineering electronic engineering information engineering Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) 021101 geological & geomatics engineering Mathematics |
| Zdroj: | Mathematical Problems in Engineering, Vol 2016 (2016) |
| ISSN: | 1024-123X |
| DOI: | 10.1155/2016/3460281 |
| Popis: | The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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