Saliency Map Generation for SAR Images with Bayes Theory and Heterogeneous Clutter Model

Deliang Xiang,Weiping Ni,Tao Tang,Han Zhang,Wentai Lei

doi:10.3390/rs9121290

Abstract

Saliency map generation in synthetic aperture radar (SAR) imagery has become a promising research area, since it has a close relationship with quick potential target identification, rescue services, etc. Due to the multiplicative speckle noise and complex backscattering in SAR imagery, producing satisfying results is still challenging. This paper proposes a new saliency map generation approach for SAR imagery using Bayes theory and a heterogeneous clutter model, i.e., the G 0 model. With Bayes theory, the ratio of the probability density functions (PDFs) in the target and background areas contributes to the saliency. Local and global background areas lead to different saliency measures, i.e., local saliency and global saliency, which are combined to make a final saliency measure. To measure the saliency of targets of different sizes, multiscale saliency enhancement is conducted with different region sizes of target and background areas. After collecting all of the salient regions in the image, the result is refined by considering the image’s immediate context. The saliency of regions that are far away from the focus of attention is suppressed. Experimental results with two single-polarization and two multi-polarization SAR images demonstrate that the proposed method has better speckle noise robustness, higher accuracy, and more stability in saliency map generation both with and without the complex background than state-of-the-art methods. The saliency map accuracy can achieve above 95% with four datasets, which is about 5–20% higher than other methods.

Highlights

Due to its all-weather and round-the-clock operational capabilities, synthetic aperture radar (SAR) has been widely used in various earth observation applications, such as urban planning, earthquake or tsunami damage assessment, military surveillance, etc. [1]
For polarimetric SAR (PolSAR) image comparison, we adopt the method proposed by Huang et al [30], which is a saliency measure method for PolSAR data based on the dissimilarity between patches, named SDPolSAR hereafter
We can see that there are some shadows near the vehicles and layovers near the buildings. These effects are common in SAR images, and we will check whether they influence the saliency map generation performance of our method in the following experiments

Summary

Introduction

Due to its all-weather and round-the-clock operational capabilities, synthetic aperture radar (SAR) has been widely used in various earth observation applications, such as urban planning, earthquake or tsunami damage assessment, military surveillance, etc. [1]. With the assumption that the backscattering of a man-made target is higher than that of the background in SAR images, CFAR discriminates the target pixels from background clutter via intensity information. This method can perform very well in a uniform background, but usually suffers some challenges when faced with a complex background. It is worth pointing out that these methods can effectively detect vehicles and ships from SAR imagery; other man-made targets, such as buildings or harbors, are not detected due to the complex structures and scattering mechanisms. It is necessary to develop a new stable method that can help identify various man-made targets in SAR imagery

Objectives

Results

Discussion

Conclusion