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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 (

SAR is an active remote sensing system that generates and transmits microwave electromagnetic radiation to the surface of a target region [

For a special application of sea area detection, SAR image is provided to detect a ship target in the sea area. And in this type of application, the SAR image needs to be firstly segmented to identify the sea area within the image. Therefore, SAR image segmentation is an important problem that requires further investigation.

Based on a review of recent studies, segmentation methods can be divided into multiple categories including feature-based methods [

Benboudjema and Pieczynski were the first to propose the triplet Markov field (TMF) model and introduced an auxiliary random field to deal with nonstationary image segmentation [

In this letter, a novel unsupervised SAR image segmentation method is proposed that is based on a hierarchical TMF in the discrete wavelet domain. The multiresolution characteristic of the discrete wavelet transform is used to represent the observed field of the SAR image as a series of discrete wavelet coefficients, so that each labeled field of the corresponding resolution can use the characteristic of the corresponding scale. A hierarchical algorithm in the discrete wavelet domain defines the relationship between different scales using a model that incorporates the global and local characteristics of an image at different scales, which can improve the efficiency of image segmentation. The WHTMF method in this paper combines the advantages of both the hierarchical algorithm and the TMF model to deal with the problem of insufficient local statistical information. Unsupervised segmentation is popular in recent years since training data is not required for parameter estimation, which is automatically accomplished and is conducive to automation of the sea area detection system. The experimental results suggest that the proposed method can improve the accuracy of SAR image segmentation.

The TMF model is developed based on the classical hidden Markov fields (HMF) mode and the pairwise Markov field (PMF) model [

Let

At the same time, let us consider that the Markov distribution of

The observed fields are described with a series of wavelet domain transformations, due to varied characteristics of discrete wavelet transform. Each scale has different characteristic vector for varying resolution, which is more conducive to reflect the nature of the observed field. The wavelet coefficients of

To model the SAR data, Zhang et al. used the generalized Gamma distribution in a hierarchical TMF model [

Let

Interaction parameters of GMRF model.

Hence, the bottom-up pass is performed as follows: firstly, compute the bottom level scale at

Quadtree structure.

Then the top-down pass is computed. At the coarsest scale

At other scales, according to the Bayesian rule, and under the guidance of the larger scale structure segmentation, the multiscale energy functions of causal TMF model are as follows:

In (

The SMAP estimator is used to segment the multiscale image [

In the unsupervised segmentation applications, the parameter estimation cannot be performed on the training data. Therefore, the expectation-maximization (EM) algorithm is used for estimation in this paper. In the iterative process of EM algorithm, the efficient maximizing pseudolikelihood (MPL) is selected with the GMRF model parameters. To compute the interaction parameters

Flow diagram of the proposed segmentation method.

In order to evaluate the segmentation quality of the proposed algorithm, the aspects of visual effects and the quantitative indications are considered. From the visual point of view, the algorithm that maintains the local features and preserves the edge information is considered as a better segmentation method. On the other hand, the Kappa coefficient and the classification error rate are mostly used in the quantitative evaluation index [

Under the hypothesis that the ideal segmentation result is known, the results of the proposed method are compared with the ideal. The pixels of the class

The classification error rate is a statistic index with significant probability. It refers to the probability of each random sample classification that is consistent with the actual classification; it is calculated as follows:

The WHTMF model is applied to the segmentation of synthetic SAR image to quantitatively describe the quality of segmentation. The segmentation results are compared with three of the traditional methods as Figure

A synthetic SAR image and the segmentation results of different models.

A synthetic SAR image

WHTMF

KMCF

HMF

TMF

It can be seen from Figure

Segmentation quality assessment of different methods.

Method | Criteria of segmentation quality assessment | |
---|---|---|

Kappa coefficient | Classification error rate | |

KMCF | 0.7483 | 0.0705 |

HMF | 0.8312 | 0.0647 |

TMF | 0.9701 | 0.0374 |

WHTMF | 0.9822 | 0.0081 |

The segmentation quality of the proposed method and the two efficient selected methods are also evaluated on real SAR images of different scenarios, as shown in Figure

SAR original images and results of segmentation by different models.

It can be seen that the segmentation results of the TMF model are better than the MRF model. The TMF model can better suppress the speckle noise and obtains more accurate segmentation compared to the MRF model. This can be attributed to the introduction of the auxiliary field

However, lacking the local structure information as guidance, the TMF model has produced some incorrect segmentation samples. It is perhaps due to the fact that the TMF model only depends on the limited pixel-level image information. According to the segmentation results, the WHTMF model can achieve better performance than the TMF model. It is observed that it produces significantly less incorrect segmentation samples compared to the TMF. Moreover, the edge information in the result of WHTMF is more accurate. These improvements can be attributed to the interscale dependencies captured by the multiscale energy function and the multiscale likelihood in our algorithm.

The WHTMF model captures the statistical property of the wavelet coefficients, and multiscale likelihoods are computed in the bottom-up pass computations. Combined with the multiscale energy function in the top-down pass computation, the conditional posterior distribution is obtained. The large structure segmentation at coarser scale can serve as a mask and allow the segmentation in finer scale to adjust. Consequently, the segmentation results by the WHTMF model show better performance.

Segmentation is the basis of sea area detection; it can provide the coordinates of the sea area from the entire SAR image. The selected region will be sent to the detector that can magnify the region for ship target detection. Recently, the system of sea area detection for ship target has the routine modules as follows. (1) Land isolation module can protect the ships target from the interference of land areas false alarm. (2) Preprocessing module restrains background clutter to highlight the ship target. (3) Target segmentation module separates the ship target from the sea background by specific algorithm of ship detection. (4) Target recognition module gets rid of the false target by a priori information of ship target. (5) Information extraction module can apply to the high resolution SAR image to extract the ship target parameters. An example is shown in Figure

Images in the sea area detection system.

Original SAR image

Segmentation of entire scene

Local magnifying image

Target extraction

Target recognition

Binary image for information extraction

Firstly, the land isolation module is used to separate the sea area from the entire scene, shown in Figure

In this letter, a novel unsupervised SAR image segmentation method is proposed that is based on hierarchical TMF in discrete wavelet domain for sea area detection. A Two-pass computation of posterior distribution involves the multiscale likelihood and multiscale energy function, and it effectively captures the global and local image characteristics. The intrascale and the interscale dependences for hierarchical TMF are considered. The experimental results show that the WHTMF model can obtain the optimized parameters and performs better segmentation of the SAR images. We use the WHTMF model in the application of sea area detection system as the first and an important basic step, and the detection of ship target is more accurate and timesaving. However, the WHTMF model studied in this letter is very simple and particularly hypothesizes that the SAR image data are Gaussian model, and this hypothesis can be relaxed to construct more general distribution models in our future research.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This research is supported by the national natural science fund project of youths science fund: The key technology research of video coding concurrent design and efficient realization (no. 61201238), and the fund project of International Exchange Program of Harbin Engineering University for Innovation oriented Talents Cultivation: The system of SAR anti-jamming effect evaluation (GH20111187).