&lt;title&gt;Multichannel SAR segmentation: algorithms and applications&lt;/title&gt;

Caves, R.G.; Quegan, S.

doi:10.1117/12.227132

Cited by 3 publications

(4 citation statements)

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“…The result of the NWLMMSE filter preserves edges and point targets better, and the speckle is also reduced to a great extent. An edge detector is often used as a tool to obtain the edge information of an image [33,34]. In this part, to qualitatively assess the edge preservation capability of the filters, the PolSAR constant false-alarm rate (CFAR) edge detector is used to detect the edge information of the images, since it is based on a test for equality of the covariance matrices and thus can utilize the full polarimetric information well [35].…”

Section: Real-world Polsar Imagesmentioning

confidence: 99%

Polarimetric SAR Speckle Filtering Using a Nonlocal Weighted LMMSE Filter

Shen¹,

Zhu³

et al. 2021

Sensors

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Despeckling is a key preprocessing step for applications using PolSAR data in most cases. In this paper, a technique based on a nonlocal weighted linear minimum mean-squared error (NWLMMSE) filter is proposed for polarimetric synthetic aperture radar (PolSAR) speckle filtering. In the process of filtering a pixel by the LMMSE estimator, the idea of nonlocal means is employed to evaluate the weights of the samples in the estimator, based on the statistical equalities between the neighborhoods of the sample pixels and the processed pixel. The NWLMMSE estimator is then derived. In the preliminary processing, an effective step is taken to preclassify the pixels, aiming at preserving point targets and considering the similarity of the scattering mechanisms between pixels in the subsequent filter. A simulated image and two real-world PolSAR images are used for illustration, and the experiments show that this filter is effective in speckle reduction, while effectively preserving strong point targets, edges, and the polarimetric scattering mechanism.

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Section: Real-world Polsar Imagesmentioning

confidence: 99%

Polarimetric SAR Speckle Filtering Using a Nonlocal Weighted LMMSE Filter

Shen¹,

Zhu³

et al. 2021

Sensors

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“…2) Multiple-channel fusion based methods [15]. These methods applied the single channel detector to detect edges for each polarimetric channel respectively, and then a fusion scheme was used to obtain the final edge map.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Edge Detection Method for Polarimetric SAR Images

2020

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The edge detection plays an important role in post-processing of PolSAR images. It is still a great challenge for extracting all the edge features and suppress speckle noises, especially when weak/strong edges appear simultaneously outside and within heterogenous areas. In this paper, a novel hybrid edge detection framework is proposed to address this problem. The proposed method is designed by fusing two initial edge detectors, which can detect complementary edge information. One is an improved polarimetric constant false alarm rate (IP_CFAR) edge detector, which can detect weak edges well, but fail to detect the edges in the heterogeneous regions. The other is the proposed weighted gradient-based (WG) detector which can detect edges in heterogeneous areas well, but loses some weak edges and produces some false edges due to the speckle noises. Secondly, based on the two detectors above, a wavelet-based hybrid edge detection method is proposed by combining their merits and suppress their shortcomings. To fuse them effectively, wavelet transformation is utilized and semantic rules are defined to extract their advantages. Moreover, a despeckling scheme is designed to suppress the false edges in the wavelet domain. Experimental results demonstrate that the proposed method outperforms the state-of-art methods in extracting both weak edges and strong edges within heterogeneous regions. INDEX TERMS Hybrid edge detection method, improved polarimetric CFAR detector, weighted gradientbased detector, wavelet fusion.

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“…Another approach is to perform segment merging in an initially over-segmented image using an appropriate test statistic. This approach has for instance been used for single channel SAR images in the MUM (Merging Using Moments) segmentation algorithm described in [5] [6].…”

Section: Introductionmentioning

confidence: 99%

“…Most of these apply to singlechannel SAR images but multi-channel algorithms have also been described. One approach for segmentation is based on edge detection and region growing as described in [3][4][5]. Another approach is to perform segment merging in an initially over-segmented image using an appropriate test statistic.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Wishart test statistics for polarimetric SAR data

Skriver

Nielsen

Conradsen

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)

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Abstract-A test statistic for equality of two covariance matrices following the complex Wishart distribution has previously been used in new algorithms for change detection, edge detection and segmentation in polarimetric SAR images. Previously, the results for change detection and edge detection have been quantitatively evaluated. This paper deals with the evaluation of segmentation. A segmentation performance measure originally developed for single-channel SAR images has been extended to polarimetric SAR images, and used to evaluate segmentation for a mergeusing-moment algorithm for polarimetric SAR data.

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<title>Multichannel SAR segmentation: algorithms and applications</title>

Cited by 3 publications

References 0 publications

Polarimetric SAR Speckle Filtering Using a Nonlocal Weighted LMMSE Filter

Polarimetric SAR Speckle Filtering Using a Nonlocal Weighted LMMSE Filter

A Novel Hybrid Edge Detection Method for Polarimetric SAR Images

Evaluation of the Wishart test statistics for polarimetric SAR data

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