SAR Image Segmentation Based on Level Set Approach and {\cal G}_A^0 Model

Marques, Régis C. P.; Medeiros, tima N.; Nobre, Juvêncio S.

doi:10.1109/tpami.2011.274

Cited by 77 publications

(70 citation statements)

References 31 publications

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“…where µ is the mean and n is the number of looks that can be estimated with the equivalent number of looks [22,33], which will be discussed later. Γ(·) is the Gamma function.…”

Section: Local and Global Single-scale Saliency Measurementioning

confidence: 99%

“…However, in its absence, the number of looks can be estimated from real SAR data, and it is therefore named the equivalent number of looks (ENL),n, [33], which can be estimated using the method of moments (MOM), as [33]:…”

Section: Parameter Estimation Of Two Pdfsmentioning

confidence: 99%

“…Among them, MOM-based methods can be easily and successfully applied to estimate the G 0 distribution parameters. According to the method presented by Marques et al and Frery et al in [33,34], the roughness parameter α can be solved by solving the following moment equation:…”

Section: Parameter Estimation Of Two Pdfsmentioning

confidence: 99%

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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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“…where µ is the mean and n is the number of looks that can be estimated with the equivalent number of looks [22,33], which will be discussed later. Γ(·) is the Gamma function.…”

Section: Local and Global Single-scale Saliency Measurementioning

confidence: 99%

Section: Parameter Estimation Of Two Pdfsmentioning

confidence: 99%

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

Xiang

Tang

et al. 2017

Remote Sensing

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“…Crandall et al, 15 with whom we compare our work, used a probabilistic framework based on graphical model inference to automatically trace layer boundaries. We present an alternative technique, similar to work proposed for using level sets to segment synthetic aperture radar imagery, 16,17 which requires greater manual interaction, but performs significantly better in some images.…”

Section: Related Workmentioning

confidence: 99%

A semi-automatic approach for estimating bedrock and surface layers from multichannel coherent radar depth sounder imagery

et al. 2013

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The dynamic responses of the polar ice sheets in Greenland and Antarctica can have substantial impacts on sea level rise. Understanding the mass balance requires accurate assessments of the bedrock and surface layers, but identifying each layer is performed subjectively by time-consuming, dense hand selection. We have developed an approach for semi-automatically estimating bedrock and surface layers from radar depth sounder imagery acquired from Antarctica. Our solution utilizes an active contours method ("level sets") to propagate an initial estimation of a layer's position based upon curvature and image intensity gradients. This allows the initial curve to gravitate with topological changes while providing smooth boundaries for discriminating between bedrock and surface layers. We evaluated the proposed semi-automatic method on 20 images with respect to hand labeled ground-truth. Compared to an automatic technique, our approach reduced labeling error by factors of 5 and 3.5 for tracing bedrock and surface layers, respectively.

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“…Statistical-based methods mainly relied on the fact that SAR images are characterized by statistical properties. Various statistical models have been proposed, e.g., Lognormal, Rayleigh, Fisher distribution [3], G 0 [4], etc. Texture-based approaches include the Gray-Level Co-occurrence Matrix (GLCM) [5], Gabor filter [6], sparse coding of wavelet polarization textons [7], etc.…”

Section: Introductionmentioning

confidence: 99%

Attribute Learning for SAR Image Classification

Liu

Kang

et al. 2017

IJGI

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This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR) images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a cross-validation step is applied to prevent overfitting. Second, the most discriminative clustering centers are sorted out to construct an attribute dictionary. By resorting to the attribute dictionary, a representation vector describing certain categories in the SAR image can be generated, which in turn is used to perform the classifying task. The experiments conducted on TerraSAR-X images indicate that those learned attributes have strong visual semantics, which are characterized by bright and dark spots, stripes, or their combinations. The classification method based on these learned attributes achieves better results.

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SAR Image Segmentation Based on Level Set Approach and {\cal G}_A^0 Model

Cited by 77 publications

References 31 publications

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

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

A semi-automatic approach for estimating bedrock and surface layers from multichannel coherent radar depth sounder imagery

Attribute Learning for SAR Image Classification

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