Akademik Veri Yönetim Sistemi
International Conference on Future Computational Technologies, İstanbul, Türkiye, 30 - 31 Mayıs 2016, cilt.1, sa.1, ss.236-239
—Hyperspectral images are high dimensional data
compared to RGB and gray level images. As well as these images
have lots of decisive feature in spectral domain, they have plenty of
information about the target also in spatial domain. Hence,
performance of target detection algorithms can be improved when
spatial and spectral features are employed together. In this paper,
Covariance Descriptor method which enable combining of spectral
and spatial features are used for target detection. In addition, Kernel
Covariance Descriptor technique is developed utilizing kernel
approach. Efficient spectral and spatial features are determined by
experimental studies. The target detection performance is compared
with each other. The experiments on two real hyperspectral data sets
demonstrate that the proposed scheme is effective for target detection
problems