NDLI: Anomaly based target detection in hyperspectral images via graph cuts

Content Provider	IEEE Xplore Digital Library
Author	Bati, E. Erdinc, A. Cesmeci, D. Caliskan, A. Koz, A. Aksoy, S. Erturk, S. Alatan, A.A.
Copyright Year	2015
Description	Author affiliation: Kocaeli Univ. Elektron. ve Haberlesme Muhendisligi, Kocaeli, Turkey (Cesmeci, D.; Erturk, S.) \|\| Bilkent Univ. Bilgisayar Muhendisligi, Ankara, Turkey (Erdinc, A.; Aksoy, S.) \|\| ODTU Goruntu Analizi Uygulama ve Arastirma Merkezi (OGAM), Turkey (Bati, E.; Caliskan, A.; Koz, A.; Alatan, A.A.)
Abstract	The studies on hyperspectral target detection until now, has been treated in two approaches. Anomaly detection can be considered as the first approach, which analyses the hyperspectral image with respect to the difference between target and the rest of the hyperspectral image. The second approach compares the previously obtained spectral signature of the target with the pixels of the hyperspectral image in order to localize the target. A distinctive disadvantage of the aforementioned approaches is to treat each pixel of the hyperspectral image individually, without considering the neighbourhood relations between the pixels. In this paper, we propose a target detection algorithm which combines the anomaly detection and signature based hyperspectral target detection approaches in a graph based framework by utilizing the neighbourhood relations between the pixels. Assuming that the target signature is available and the target sizes are in the range of anomaly sizes, a novel derivative based matched filter is first proposed to model the foreground. Second, a new anomaly detection method which models the background as a Gaussian mixture is developed. The developed model estimates the optimal number of components forming the Gaussian mixture by means of utilizing sparsity information. Finally, the similarity of the neighbouring hyperspectral pixels is measured with the spectral angle mapper. The overall proposed graph based method has successfully combined the foreground, background and neighbouring information and improved the detection performance by locating the target as a whole object free from noises.
Starting Page	2631
Ending Page	2634
File Size	5733195
Page Count	4
File Format	PDF
ISBN	9781467373869
DOI	10.1109/SIU.2015.7130428
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-16
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	anomaly spectral signature Object detection hyperspectral image graph cuts Minimization Electronic mail Hyperspectral imaging
Content Type	Text
Resource Type	Article

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