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Proceedings Paper

Covariance descriptor fusion for target detection
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Paper Abstract

Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

Paper Details

Date Published: 17 May 2016
PDF: 8 pages
Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98421B (17 May 2016); doi: 10.1117/12.2223765
Show Author Affiliations
Huseyin Cukur, Yildiz Technical Univ. (Turkey)
Hamidullah Binol, Yildiz Technical Univ. (Turkey)
Abdullah Bal, Yildiz Technical Univ. (Turkey)
Fatih Yavuz, Yildiz Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 9842:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXV
Ivan Kadar, Editor(s)

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