Saliency Map Generation for SAR Images with Bayes Theory and Heterogeneous Clutter Model
Northwest Institute of Nuclear Technology, Xi’an 710024, China
College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
School of Information Science and Engineering, Central South University, Changsha 410004, China
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 7 December 2017 / Accepted: 9 December 2017 / Published: 11 December 2017
Saliency map generation in synthetic aperture radar (SAR) imagery has become a promising research area, since it has a close relationship with quick potential target identification, rescue services, etc. Due to the multiplicative speckle noise and complex backscattering in SAR imagery, producing satisfying results is still challenging. This paper proposes a new saliency map generation approach for SAR imagery using Bayes theory and a heterogeneous clutter model, i.e., the
model. With Bayes theory, the ratio of the probability density functions (PDFs) in the target and background areas contributes to the saliency. Local and global background areas lead to different saliency measures, i.e., local saliency and global saliency, which are combined to make a final saliency measure. To measure the saliency of targets of different sizes, multiscale saliency enhancement is conducted with different region sizes of target and background areas. After collecting all of the salient regions in the image, the result is refined by considering the image’s immediate context. The saliency of regions that are far away from the focus of attention is suppressed. Experimental results with two single-polarization and two multi-polarization SAR images demonstrate that the proposed method has better speckle noise robustness, higher accuracy, and more stability in saliency map generation both with and without the complex background than state-of-the-art methods. The saliency map accuracy can achieve above 95% with four datasets, which is about 5–20% higher than other methods.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Xiang, D.; Tang, T.; Ni, W.; Zhang, H.; Lei, W. Saliency Map Generation for SAR Images with Bayes Theory and Heterogeneous Clutter Model. Remote Sens. 2017, 9, 1290.
Xiang D, Tang T, Ni W, Zhang H, Lei W. Saliency Map Generation for SAR Images with Bayes Theory and Heterogeneous Clutter Model. Remote Sensing. 2017; 9(12):1290.
Xiang, Deliang; Tang, Tao; Ni, Weiping; Zhang, Han; Lei, Wentai. 2017. "Saliency Map Generation for SAR Images with Bayes Theory and Heterogeneous Clutter Model." Remote Sens. 9, no. 12: 1290.
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