Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters

Uslu, Erkan; Albayrak, Songul

doi:10.3390/rs6065497

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…These approaches capture edges better than the wavelet transform owing to their high directional sensitivity and anisotropy. The curvelet transform therefore has been widely applied in the image processing field [11][12][13][14][15]. A contrast enhancement method based on curvelet transform has been developed, which uses a gain function with four parameters to modify the curvelet transform coefficients [11].…”

Section: Introductionmentioning

confidence: 99%

Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain

Zhou

Huo

2015

Mathematical Problems in Engineering

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We propose a novel automatic side-scan sonar image enhancement algorithm based on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Firstly, the noisy and low-contrast sonar image is decomposed into a low frequency channel and a series of high frequency channels by using curvelet transform. Secondly, a new nonlinear mapping scheme, which coincides with the logarithmic nonlinear enhancement characteristic of the HVS perception, is designed without any parameter tuning to adjust the curvelet transform coefficients in each channel. Finally, the enhanced image can be reconstructed with the modified coefficients via inverse curvelet transform. The enhancement is achieved by amplifying subtle features, improving contrast, and eliminating noise simultaneously. Experiment results show that the proposed algorithm produces better enhanced results than state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%

Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain

Zhou

Huo

2015

Mathematical Problems in Engineering

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“…Therefore, extracting spatial and geometric structure information of buildings from VHR SAR images is a highly attractive problem [2,6]. However, complex environmental factors [4,7], different building orientations [4,8] and their heterogeneity [9], speckle [10], and acquisition geometry, made building extraction from SAR data an open challenge [2,11].…”

Section: Introductionmentioning

confidence: 99%

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

Gui

Hui

et al. 2016

Remote Sensing

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Accurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR) images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting individual buildings from SAR images based on domain ontology. By analyzing a building scattering model with different orientations and structures, the building ontology model is set up to express multiple characteristics of individual buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the individual building objects expression based on an ontological semantic description is formed. Finally, the building primitives are used to detect buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof buildings larger than 250 pixels with different orientations.

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Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters

Cited by 2 publications

References 17 publications

Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain

Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

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