Fast and Accurate Target Detection Based on Multiscale Saliency and Active Contour Model for High-Resolution SAR Images

Tu, Song; Su, Yi

doi:10.1109/tgrs.2016.2571309

Cited by 34 publications

(15 citation statements)

References 32 publications

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“…There are three kinds of approaches for outlining the detected target, the connected domain or contour-based approach [22,43], the clustering-based approach [25], and the sliding window-based approach [44]. To efficiently identify the candidate target region, a mean-shift based method, which is similar with the approach in [44], is applied.…”

Section: Proposed Methodsmentioning

confidence: 99%

Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

Pan

Liu

Qiu

et al. 2017

Sensors

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This study aims to detect vessels with lengths ranging from about 70 to 300 m, in Gaofen-3 (GF-3) SAR images with ultrafine strip-map (UFS) mode as fast as possible. Based on the analysis of the characteristics of vessels in GF-3 SAR imagery, an effective vessel detection method is proposed in this paper. Firstly, the iterative constant false alarm rate (CFAR) method is employed to detect the potential ship pixels. Secondly, the mean-shift operation is applied on each potential ship pixel to identify the candidate target region. During the mean-shift process, we maintain a selection matrix recording which pixels can be taken, and these pixels are called as the valid points of the candidate target. The l1 norm regression is used to extract the principal axis and detect the valid points. Finally, two kinds of false alarms, the bright line and the azimuth ambiguity, are removed by comparing the valid area of the candidate target with a pre-defined value and computing the displacement between the true target and the corresponding replicas respectively. Experimental results on three GF-3 SAR images with UFS mode demonstrate the effectiveness and efficiency of the proposed method.

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Section: Proposed Methodsmentioning

confidence: 99%

Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

Pan

Liu

Qiu

et al. 2017

Sensors

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“…There are many target detection algorithms proposed for SAR and PolSAR images [3][4][5][6][7][8][9][10]. Among them, the constant false alarm rate (CFAR) [11] is the classic and most popular detection method, and has been widely used.…”

Section: Introductionmentioning

confidence: 99%

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

Xiang

Tang

et al. 2017

Remote Sensing

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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.

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“…However, this model involves high computational complexity. In addition, the model is sensitive to the initialization location and parameters [5,26]. Wang et al [27] defined an energy functional that combines the merits of the C-V model and the LBF model [21].…”

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confidence: 99%

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Sun

Meng

et al. 2018

Applied Sciences

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Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF-and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.2 of 20 an object with strong edges; however, they cannot detect the weak edges of an object. Moreover, the methods are sensitive to noise and do not easily obtain satisfactory segmentation results for blurred images [2]. In addition, the contour should initially be set near the object; otherwise, it is difficult to obtain correct segmentation results [17]. Region-based models make full use of image statistical information, whereas edge-based models do not. Thus, region-based models have multiple advantages over edge-based models. For example, because regional information is used, region-based models are less sensitive to contour initialization and noise. Furthermore, these region-based models can easily segment images with weak boundaries or even those without boundaries [18]. One of the most typical region-based methods was proposed by Chan and Vese (C-V) [11], which is based on the Mumford-Shah functional [19]. The C-V model is based on the assumption that image intensities are homogeneous in each region. However, this assumption does not suit the intensity of inhomogeneous images, which limits the method's further applications [20,21].Recently, hybrid methods have gained popularity among region-based methods. These methods combine region (local or global) and edge information in their energy formulations [22]. Zhang et al. [23] proposed the selective binary and Gaussian filtering regularized level set (SBGFRLS) model. This model combines the advantages of region-based and edge-based active contours and introduces a region-based SPF function, which utilizes the image global intensity means from the C-V method. This metho...

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Fast and Accurate Target Detection Based on Multiscale Saliency and Active Contour Model for High-Resolution SAR Images

Cited by 34 publications

References 32 publications

Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

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

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

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