1. Unsupervised SAR Image Segmentation Based on a Hierarchical TMF Model in the Discrete Wavelet Domain for Sea Area Detection
- Author
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Jiajing Wang, Shuhong Jiao, Zhenyu Sun, Lianyang Shen, and Lin Tang
- Subjects
Synthetic aperture radar ,Article Subject ,Computer science ,business.industry ,lcsh:Mathematics ,Posterior probability ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Scale-space segmentation ,Image segmentation ,lcsh:QA1-939 ,law.invention ,Wavelet ,ComputingMethodologies_PATTERNRECOGNITION ,law ,Modeling and Simulation ,Computer Science::Computer Vision and Pattern Recognition ,Segmentation ,Computer vision ,Artificial intelligence ,Radar ,business ,Energy (signal processing) ,Physics::Atmospheric and Oceanic Physics - Abstract
Unsupervised synthetic aperture radar (SAR) image segmentation is a fundamental preliminary processing step required for sea area detection in military applications. The purpose of this step is to classify large image areas into different segments to assist with identification of the sea area and the ship target within the image. The recently proposed triplet Markov field (TMF) model has been successfully used for segmentation of nonstationary SAR images. This letter presents a hierarchical TMF model in the discrete wavelet domain of unsupervised SAR image segmentation for sea area detection, which we have named the wavelet hierarchical TMF (WHTMF) model. The WHTMF model can precisely capture the global and local image characteristics in the two-pass computation of posterior distribution. The multiscale likelihood and the multiscale energy function are constructed to capture the intrascale and intrascale dependencies in a random field (X,U). To model the SAR data related to radar backscattering sources, the Gaussian distribution is utilized. The effectiveness of the proposed model for SAR image segmentation is evaluated using synthesized and real SAR data.
- Published
- 2014
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