Hyperspectral Target Detection Based on Target-Constrained Interference-Minimized Band Selection
| Autor: | Chien-I Chang, Haoyang Yu, Xiaodi Shang, Meiping Song, Yulei Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Dimensionality reduction Feature extraction 0211 other engineering and technologies Hyperspectral imaging Pattern recognition 02 engineering and technology Interference (wave propagation) Object detection Task (project management) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Set (psychology) business 021101 geological & geomatics engineering |
| Zdroj: | IGARSS |
| Popis: | Hyperspectral imagery provides wealthy spectral information to make it suitable for many applications. However, for specific applications, extracting suitable bands from high-dimensional data is a tedious and difficult task. In the past, many methods have been developed to perform band selection for specific tasks such as target detection. However, there is very little work to consider and deal with the effects of suspected interfering targets. In this paper, a new method for band selection, called target-constrained interference-minimized band selection (TCIMBS) is developed for specific target detection. It can select a band set with strong characterization capabilities for desired targets and good suppression for undesired targets and background (BKG). Experimental results demonstrate that TCIMBS can improve the detection performance, and also achieve better performances in comparison with several state-of-the-art methods. |
| Databáze: | OpenAIRE |
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