Differences between the spatial pattern (pixel intensity and distribution) of targets and clutter allow target segmentation to be achieved by analyzing spatial patterns in Synthetic Aperture Radar (SAR) images. This paper thus proposes a target segmentation method for SAR images based on the appearance conversion machine theory. The proposed method analyses the spatial patterns in SAR images and calculates the degree of similarity between the SAR image and the reference clutter images. Subsequently, regions that show high similarity to reference clutter images are erased so that segmentation can be achieved. To evaluate the degree of similarity, we also use an automatic threshold selection method based on the cumulative histogram of the similarity imge. Experimental results using simulation and real data verify the effectiveness of the proposed method."
程江华, 高贵, 库锡树, 等. 高分辨率SAR图像道路交叉口检测与识别新方法[J]. 雷达学报, 2012, 1(1): 100-108. Cheng Jiang-hua, Gao Gui, Ku Xi-shu, et al.. A novel method for detecting and identifying road junctions from high resolution SAR images[J]. Journal of Radars, 2012, 1(1): 100-108.
[2]
李光廷, 杨亮, 黄平平, 等. SAR图像相干斑抑制中的像素相关性测量[J]. 雷达学报, 2012, 1(3): 301-308. Li Guang-ting, Yang Liang, Huang Ping-ping, et al.. The pixel-similarity measurement in SAR image despeckling[J]. Journal of Radars, 2012, 1(3): 301-308.
[3]
Sauvola J and Pietikäinen M. Adaptive document image binarization[J]. Pattern Recognition, 2000, 33(2): 225-236.
[4]
Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.
[5]
Zhao X, Jiang Y, and Zhang Y. Automatic binarization method in ISAR image[C]. IEEE International Geoscience and Remote Sensing Symposium, Milan, 2015: 5415-5418.
[6]
Stagliano D, Lupidi A, Berizzi F, et al.. Exploitation of COSMO-SkyMed system for detection of ships responsible for oil spills[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, 2012: 915-918.
[7]
Leng X, Ji K, Yang K, et al.. A bilateral CFAR algorithm for ship detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1536-1540.
[8]
Liao M, Wang C, Wang Y, et al.. Using SAR images to detect ships from sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(2): 194-198.
[9]
Mcconnell A I and Oliver C J. Comparison of segmentation methods with standard CFAR for point target detection[C]. Proceedings SPIE 3497, SAR Image Analysis, Modeling, and Techniques, 1998. doi: 10.1117/12.331364.
[10]
Lankoande O, Hayat M M, and Santhanam B. Segmentation of SAR images based on Markov random field model[C]. IEEE International Conference on Systems, Man and Cybernetics, 2005, 3: 2956-2961.
[11]
HUANG Yu, FU Kun, and WU Yi-Rong. Image segmentation method using K-means based on Markov random field[J]. Acta Electronica Sinica, 2009, 37(12): 2700-2704.
[12]
Fowlkes C, Belongie S, Fan C, et al.. Spectral grouping using the Nyström method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2): 214-225.
[13]
Zhang X, Hao L, Liu F, et al.. Spectral clustering ensemble applied to SAR image segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(7): 2126-2136.
[14]
Kusakunniran W, Wu Q, Zhang J, et al.. A new view-invariant feature for cross-view gait recognition[J]. IEEE Transactions on Information Forensics and Security, 2013, 8(10): 1642-1653.
[15]
Zhao X, Jiang Y, Stathaki T, et al.. Gait recognition method for arbitrary straight walking paths using appearance conversion machine[J]. Neurocomputing, 2015, 173(3): 530-540.
[16]
Huang G B, Zhou H, Ding X, et al.. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2012, 42(2): 513-529.
[17]
Huang G B, Chen L, and Siew C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes[J]. IEEE Transactions on Neural Networks, 2006, 17(4): 879-892.
[18]
Huang G B and Chen L. Enhanced random search based incremental extreme learning machine[J]. Neurocomputing, 2008, 71(16/18): 3460-3468.
[19]
Huang G B, Zhu Q Y, and Siew C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/3): 489-501.
[20]
Gao G, Zhao L, Zhang J, et al.. A segmentation algorithm for SAR images based on the anisotropic heat diffusion equation[J]. Pattern recognition, 2008, 41(10): 3035-3043.
