A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data

Yue, Xiaoyang; Teng, Fei; Lin, Yun; Hong, Wen

doi:10.1109/jstars.2021.3127537

Cited by 5 publications

(21 citation statements)

References 41 publications

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“…It can be seen that a single feature usually describes the target only from a certain aspect, and there are often many false alarms in the results. However, the existing feature fusion methods cannot balance the weights of different features well [22], and it is easy to make the final result greatly affected by one of the features so the detection effect is not very good. In order to solve this problem, a new fusion method is proposed based on a comprehensive analysis of the above two features.…”

Section: Multi-feature Extraction Results Fusionmentioning

confidence: 99%

“…As mentioned above, current anisotropic scattering analysis based on CSAR images is mostly combined with polarization information, while based on single-polarization SAR images, most methods use different statistical distribution models for analysis; that is, with the change of observation aspect, the probability density function (PDF) of an anisotropic target is different, while the PDF of an isotropic target is basically stable. By establishing binary hypotheses and using the likelihood ratio test, anisotropic or isotropic targets can be extracted by threshold [19,20,22]. However, such methods mainly have the following shortcomings: (1) For SAR images with complex scenes, the common distribution model cannot fit well, resulting in inaccurate extraction results.…”

Section: Anisotropic Scattering At the Different Azimuth Aspectsmentioning

confidence: 99%

“…Therefore, a large number of improved algorithms have appeared. These algorithms usually introduce spatial neighborhood information into the objective function [22], and the improved objective function is usually shown as follows:…”

Section: A Fast Fcm Algorithmmentioning

confidence: 99%

“…Obviously, the calculation of aspect entropy is very simple, and the angle dimension information in multi-aspect SAR images can be quickly extracted by this definition. Compared with the existing likelihood ratio detection methods [19,20,22], it is more conducive to practical application. Since aspect entropy is calculated directly by the gray level of the image, it is greatly affected by noise.…”

Section: Aspect Entropymentioning

confidence: 99%

“…Therefore, some scholars adopted the method of fusing multiple features to achieve a more complete target extraction. For example, Yue et al used statistical distribution and membership to extract anisotropic scattering features and amplitude features of man-made targets, respectively [22], but the statistical distribution parameters of this method are difficult to estimate, and the sliding window size of the distribution model needs to be selected manually. Liu et al first extracted multiple feature variances to obtain a comprehensive description of the target change pattern, then obtained finer feature vectors through principal component analysis, and finally used support vector machines to achieve automatic extraction of building areas in complex scenes [23].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Building Contour Extraction Based on CSAR Images

Jia

Chen

2023

Remote Sensing

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Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction based on CSAR images mostly uses anisotropic scattering features. For special targets such as buildings, as the walls and the ground form dihedral corner structures, there are also obvious strong scattering features such as double-scattering lines in SAR images. Therefore, combining the strong scattering features of buildings at specific aspects with anisotropic scattering characteristics at different aspects can obtain better extraction results, and how to extract these features accurately and efficiently is the key point. Based on this, this paper proposes a novel method for building contour extraction based on CSAR images. For strong scattering features, a fast fuzzy C-means (FCM) clustering algorithm was used to extract them. For anisotropic scattering features, aspect entropy was used to characterize the anisotropy degree, and K-means clustering was combined to extract. Finally, a more accurate result is obtained by merging the two feature extraction results. In order to verify the effectiveness and practicability of the proposed method, a lot of measured data acquired by the self-developed airborne L-band and Ku-band CSAR systems were processed. The experiments show that, compared with state-of-the-art algorithms, the proposed method can obtain more accurate results in less time.

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Section: Multi-feature Extraction Results Fusionmentioning

confidence: 99%

Section: Anisotropic Scattering At the Different Azimuth Aspectsmentioning

confidence: 99%

Section: A Fast Fcm Algorithmmentioning

confidence: 99%

Section: Aspect Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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