SAR Image Segmentation Using the Roughness Information

Rodrigues, Francisco Alixandre Ávila; Neto, J. F. S. Rocha; Marques, Régis C. P.; Medeiros, Fátima N. S. de; Nobre, Juvêncio S.

doi:10.1109/lgrs.2015.2496340

Cited by 26 publications

(9 citation statements)

References 19 publications

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“…Studying the surface roughness geometry, especially studying the natural surfaces, requires appropriate satellite data and processing methodologies [1][2][3]. The Synthetic Aperture Radar (SAR) data acquired by the airborne and space-borne sensors has made it possible to examine the surface roughness, which provides useful information for geoscientists and geologists.…”

Section: Introductionmentioning

confidence: 99%

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

et al. 2017

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Abstract:Microwave remote sensing can measure surface geometry. Via the processing of the Synthetic Aperture Radar (SAR) data, the earth surface geometric parameters can be provided for geoscientific studies, especially in geological mapping. For this purpose, it is necessary to model the surface roughness against microwave signal backscattering. Of the available models, the Integral Equation Model (IEM) for co-polarized data has been the most frequently used model. Therefore, by the processing of the SAR data using this model, the surface geometry can be studied. In the IEM, the surface roughness geometry is calculable via the height statistical parameter, the rms-height. However, this parameter is not capable enough to represent surface morphology, since it only measures the surface roughness in the vertical direction, while the roughness dispersion on the surface is not included. In this paper, using the random fractal geometry capability, via the implementation of the power-law roughness spectrum, the precision and correctness of the surface roughness estimation has been improved by up to 10%. Therefore, the random fractal geometry is implemented through the calculation of the input geometric parameters of the IEM using the power-law surface spectrum and the spectral slope. In this paper, the in situ roughness measurement data, as well as SAR images at frequencies of L, C, and X, have been used to implement and evaluate the proposed method. Surface roughness, according to the operational frequencies, exhibits a fractal or a diffractal behavior.

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Section: Introductionmentioning

confidence: 99%

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

et al. 2017

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“…The weighting used for the SAR and DSM is a very simple one. For SAR, more sophisticated models exist that were already used for segmentation with good results (Rodrigues et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Sar and Oblique Aerial Optical Image Fusion for Urban Area Image Segmentation

Fagir

Schubert

Frioud

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform -leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

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“…We answer these questions by demonstrating, for the first time, an efficient and flexible classification algorithm that utilizes the in-scene MSAR-derived motion information. This method complements existing approaches to classification [11][12][13][14][15][16][17][18][19] and image segmentation [20][21][22][23][24][25][26][27] that exploit the spatial structure of static amplitude images. Our experimental results, performed on imagery captured by the U.S.…”

Section: Introductionmentioning

confidence: 95%

Multi-Channel Synthetic Aperture Radar Based Classification of Maritime Scenes

et al. 2020

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We present novel experimental evidence that demonstrates the effectiveness of exploiting scene motion information for the analysis of scene structure in maritime imaging applications. We analyze data captured by our novel airborne Multi-channel SAR (MSAR) system that is particularly suited to sampling the velocity profile of scatterers in the maritime environment. While previous works have shown the utility MSAR systems for correcting scene motion induced blurring artifacts, our work shows, for the first time, how the information furnished by an MSAR system can systematically render accurate classification of maritime scenes into different perceptual categories. We offer a methodology that is superior to traditional classification techniques that are based purely on the spatial structure of an image. Furthermore, the simplicity of the feature space involved together with the demonstrated classification performance on imagery captured by our airborne MSAR system underscore the strength of the methodology. INDEX TERMS Multi-channel synthetic aperture radar (SAR), ocean imaging, image classification

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SAR Image Segmentation Using the Roughness Information

Cited by 26 publications

References 19 publications

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data

Sar and Oblique Aerial Optical Image Fusion for Urban Area Image Segmentation

Multi-Channel Synthetic Aperture Radar Based Classification of Maritime Scenes

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