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.
Highlights
synthetic aperture radar (SAR) is an active remote sensing system that generates and transmits microwave electromagnetic radiation to the surface of a target region [1]
A novel unsupervised SAR image segmentation method is proposed that is based on a hierarchical triplet Markov field (TMF) in the discrete wavelet domain
The experimental results suggest that the proposed method can improve the accuracy of SAR image segmentation
Summary
SAR is an active remote sensing system that generates and transmits microwave electromagnetic radiation to the surface of a target region [1]. Wang et al. Discrete Dynamics in Nature and Society proposed a change-detection method for segmentation based on the TMF model. Discrete Dynamics in Nature and Society proposed a change-detection method for segmentation based on the TMF model She redefined the third field U in the TMF model to describe the nonstationary textural similarity between two multitemporal SAR images [10]. Lian et al proposed an unsupervised SAR image segmentation algorithm based on a conditional TMF framework which combines the advantages of both CRF and TMF [12]. A novel unsupervised SAR image segmentation method is proposed that is based on a hierarchical TMF in the discrete wavelet domain. The experimental results suggest that the proposed method can improve the accuracy of SAR image segmentation
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