Target detection of hyperspectral image based on spectral saliency. Issue 2 (1st February 2019)

• Record Type: Journal Article
• Authors: Zhang, Xiaorong; Pan, Zhibin; Hu, Bingliang; Zheng, Xi; Liu, Weihua
• Is Part Of: IET image processing. Volume 13:Issue 2(2019)
• Page Start: 316
• Page End: 322
• Publication Date: 2019-02-01
• Subjects: hyperspectral imaging -- object detection -- remote sensing -- geophysical image processing; hyperspectral image -- remote sensing -- unsupervised target detection -- spectral saliency target detection -- HSI set -- spectral matching algorithm -- SSD algorithm; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.1173
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Target detection of hyperspectral image (HSI) is a research hotspot in the field of remote sensing. It is of particular importance in many domains, especially in military application. Unsupervised target detection is usually more difficult because there is no prior information about target. Traditional algorithms exploit spectral information, only. This study introduces the idea of saliency detection from the visual technique into HSI processing domain and proposes a novel approach named spectral saliency target detection (SSD). It establishes a novel salient model, which utilises both spatial saliency and spectral saliency. In the framework of SSD, it combines the model with spectral matching algorithm to make it perform well even in situations where the target is concealed and small. A HSI set comprised of eight different scenes with complex background is setup to evaluate the performance of the proposed algorithm. The final visible detection results demonstrate that the SSD algorithm outperforms the others. The receiver operation characteristic (ROC) curve and area under the ROC curve are applied to evaluate the results. The proposed algorithm shows superior and stable performance.