Hierarchical semantic model and scattering mechanism based PolSAR image classification

Liu, Fang; Shi, Jianyou; Jiao, Licheng; Liu, Hongying; Yang, Shuyuan; Wu, Jie; Hao, Hongxia; Yuan, Jialing

doi:10.1016/j.patcog.2016.02.020

Cited by 48 publications

(14 citation statements)

References 58 publications

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“…A polarimetric sketch map 16 is extracted as the sparse image representation by considering polarimetric mechanism and speckle model. The polarimetric sketch model mainly contains two steps: the polarimetric edge-line detection and the selection of sketch lines.…”

Section: Polarimetric Sketch Modelmentioning

confidence: 99%

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Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field

Shi

Liu

Jiao

et al. 2016

J. Appl. Remote Sens

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Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field,"Abstract. Markov random field (MRF) model is an effective tool for polarimetric synthetic aperture radar (PolSAR) image classification. However, due to the lack of suitable contextual information in conventional MRF methods, there is usually a contradiction between edge preservation and region homogeneity in the classification result. To preserve edge details and obtain homogeneous regions simultaneously, an adaptive MRF framework is proposed based on a polarimetric sketch map. The polarimetric sketch map can provide the edge positions and edge directions in detail, which can guide the selection of neighborhood structures. Specifically, the polarimetric sketch map is extracted to partition a PolSAR image into structural and nonstructural parts, and then adaptive neighborhoods are learned for two parts. For structural areas, geometric weighted neighborhood structures are constructed to preserve image details. For nonstructural areas, the maximum homogeneous regions are obtained to improve the region homogeneity. Experiments are taken on both the simulated and real PolSAR data, and the experimental results illustrate that the proposed method can obtain better performance on both region homogeneity and edge preservation than the state-of-the-art methods.

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Section: Polarimetric Sketch Modelmentioning

confidence: 99%

“…Recently, according to Marr's computer vision theory, 15 a polarimetric sketch map 16 is extracted to partition a PolSAR image into structural and nonstructural parts by considering scattering characteristics and speckle noises of PolSAR images. Sketch segments with length and orientation are obtained in the polarimetric sketch map.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field

Shi

Liu

Jiao

et al. 2016

J. Appl. Remote Sens

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“…Therefore, a proper fusion scheme, which can both keep the advantages of the two detectors and get rid of the shortcomings, is rather important. For this problem, some fusion functions [30], [32] have been proposed by firstly normalizing the data of the two edge energy maps. However, two sets of data are hardly to be fused accurately by comparing corresponding pixels directly since they have different distributions.…”

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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“…The classification is arranging the pixels to the different categories according to the certain rule. The common objects within the PolSAR images include land, buildings, water, sand, urban areas, vegetation, road, bridge and so on [2]. In order to distinguish them, the features of the pixels should be fully extracted and mined.…”

Section: Introductionmentioning

confidence: 99%

Polsar Image Classification Based on Polarimetric Scattering Coding and Sparse Support Matrix Machine

Liu

Jiao

Zhang

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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POLSAR image has an advantage over optical image because it can be acquired independently of cloud cover and solar illumination. PolSAR image classification is a hot and valuable topic for the interpretation of POLSAR image. In this paper, a novel POLSAR image classification method is proposed based on polarimetric scattering coding and sparse support matrix machine. First, we transform the original POLSAR data to get a real value matrix by the polarimetric scattering coding, which is called polarimetric scattering matrix and is a sparse matrix. Second, the sparse support matrix machine is used to classify the sparse polarimetric scattering matrix and get the classification map. The combination of these two steps takes full account of the characteristics of POLSAR. The experimental results show that the proposed method can get better results and is an effective classification method.Index Terms-POLSAR image, classification, scattering coding, sparse support matrix machine.

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Hierarchical semantic model and scattering mechanism based PolSAR image classification

Cited by 48 publications

References 58 publications

Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field

Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field

A Novel Hybrid Edge Detection Method for Polarimetric SAR Images

Polsar Image Classification Based on Polarimetric Scattering Coding and Sparse Support Matrix Machine

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