An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

Dongdong Yang,Rong Fei,Hui Yang

doi:10.3390/a8010032

Abstract

Synthetic aperture radar (SAR) image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous nonlocal mean (NLM) filter is introduced in this study to suppress the multiplicative speckle noise in SAR image. Furthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the searching window are projected into a lower dimensional subspace by principal component analysis (PCA). Thus, the nonlocal mean filter is implemented in the subspace. Afterwards, a multi-objective clustering algorithm is proposed using the principals of artificial immune system (AIS) and kernel-induced distance measures. The multi-objective clustering has been shown to discover the data distribution with different characteristics and the kernel methods can improve its robustness to noise and outliers. Experiments demonstrate that the proposed method is able to partition the SAR image robustly and accurately than the conventional approaches.

Highlights

Synthetic Aperture Radar (SAR) is an advanced microwave equipment of earth observation and has obtained much more interests for it working at all-weather and all-time, strong permeability, multi-bands and polarization information [1]
It is clear that the three variants of fuzzy c-means (FCM) cannot achieve good segmentation results of the image
The reason may be that the similarity measures designed by incorporating local spatial and gray information cannot produce enough suppressing results of the speckle noise

Summary

Introduction

Synthetic Aperture Radar (SAR) is an advanced microwave equipment of earth observation and has obtained much more interests for it working at all-weather and all-time, strong permeability, multi-bands and polarization information [1]. The images obtained by SAR are generally degenerated by speckle noise because of its distinct and special imaging mechanism. The existence of speckle usually leads to the degradation of image quality and has a directly impact on the SAR image recognition and interpretation. Another important issue in SAR image understanding is how to efficiently distinguish different land covers in this type of images. The above two issues are two main crucial points in SAR image segmentation. An efficient SAR image segmentation methodology should consider these two issues simultaneously

Methods

Results

Conclusion