SAR image edge detection via sparse representation

Ma, Xiaole; Liu, Shuaiqi; Hu, Shaohai; Geng, Peng; Liu, Ming; Zhao, Jie

doi:10.1007/s00500-017-2505-y

Cited by 29 publications

(13 citation statements)

References 17 publications

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“…To overcome these challenges, multiple of edge detection methods [9]- [17] have been studied for decade years. There are three major thoughts for edge detection in Pol-SAR images according to the literature available.…”

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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Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Edge Detection Method for Polarimetric SAR Images

2020

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“…In DST, the probabilities are assigned to basic probability assignments (BPAs), which is presented to generalize the BPA in probability distribution in PT. The DST has shown its effectiveness and advantages in wide applications with uncertainty in terms of decision making, such as knowledge reasoning [ 7 , 8 , 9 ], sensor fusion [ 10 , 11 , 12 , 13 ], reliability analysis [ 14 , 15 ], fault diagnosis [ 16 , 17 , 18 ], assessment and evaluation [ 19 , 20 , 21 ], image recognition [ 22 , 23 ], and others [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Belief Entropy for Measuring Uncertainty in Dempster-Shafer Evidence Theory Framework Based on Plausibility Transformation and Weighted Hartley Entropy

Pan

Zhou

Tang

et al. 2019

Entropy

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Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST framework is still an open issue. The main work of this study is to define a new belief entropy for measuring uncertainty of BPA. The proposed belief entropy has two components. The first component is based on the summation of the probability mass function (PMF) of single events contained in each BPA, which are obtained using plausibility transformation. The second component is the same as the weighted Hartley entropy. The two components could effectively measure the discord uncertainty and non-specificity uncertainty found in DST framework, respectively. The proposed belief entropy is proved to satisfy the majority of the desired properties for an uncertainty measure in DST framework. In addition, when BPA is probability distribution, the proposed method could degrade to Shannon entropy. The feasibility and superiority of the new belief entropy is verified according to the results of numerical experiments.

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“…synthetic Aperture Radar (SAR) images are more difficult to understand, because of complicated interaction of microwaves with the Earth surface objects and speckle noise. They require careful analysis [1][2]. The main aim of the analysis is to streamline the description of the image data into quantifiable information which is realistic, highly understandable and accessible to use it as an input for applications.…”

Section: Introductionmentioning

confidence: 99%

Pixel Based Sar Image Classification using Random Forest Algorithm

Balnarsaiah¹,

Prasad²,

Laxminarayana³

2019

IJITEE

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Synthetic Aperture Radar (SAR) images (Microwave data) were classified using Multi-Layer Feed Forward, Cascade Forward Neural Networks and Random Forest (RF) algorithms. For the Random Forest, a general model for classification of Remotely Sensed Radar dual-polarization data based on RF is implemented and classified of SAR image (microwave data) classifications. The RF model exploits spatial context between neighbouring pixels in an image, and temporal class dependencies between different images of the same region, in the case of multi-temporal data. Based on the well-founded experimental on basis of random forest techniques for classification tasks and the encouraging experimental results in RF algorithm , the authors conclude that the proposed RF algorithm is useful for classification of SAR (Sentinel 1A) imagery and evaluate its accuracy and kappa coefficient.

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SAR image edge detection via sparse representation

Cited by 29 publications

References 17 publications

A Novel Hybrid Edge Detection Method for Polarimetric SAR Images

A Novel Hybrid Edge Detection Method for Polarimetric SAR Images

A Novel Belief Entropy for Measuring Uncertainty in Dempster-Shafer Evidence Theory Framework Based on Plausibility Transformation and Weighted Hartley Entropy

Pixel Based Sar Image Classification using Random Forest Algorithm

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