4 research outputs found

    An optimal multiedge detector for SAR image segmentation,”

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    Abstract-Edge detection is a fundamental issue in image analysis. Due to the presence of speckle, which can be modeled as a strong, multiplicative noise, edge detection in synthetic aperture radar (SAR) images is extremely difficult, and edge detectors developed for optical images are inefficient. Several robust operators have been developed for the detection of isolated step edges in speckled images. We propose a new step-edge detector for SAR images, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model. It computes a normalized ratio of exponentially weighted averages (ROEWA) on opposite sides of the central pixel. This is done in the horizontal and vertical direction, and the magnitude of the two components yields an edge strength map. Thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme. Experimental results obtained from simulated SAR images as well as ERS-1 data are presented. Index Terms-Edge detection, multiedge model, region merging, segmentation, speckle, synthetic aperture radar (SAR), watershed algorithm

    SAR Image Edge Detection: Review and Benchmark Experiments

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    Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art

    Digital Surface Modelling in Developing Countries Using Spaceborne SAR Techniques

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    Topographic databases at the national level, in the form of Digital Surface Models (DSMs), are required for a large number of applications which have been spurred on by the increased use of Geographic Information Systems (GIS). Ground-Based (surveying, GPS, etc.) and traditional airborne approaches to generating topographic information are proving to be time consuming and costly for applications in developing countries. Where these countries are located in the tropical zone, they are affected by the additional problem of cloud cover which could cause delays for almost 75% of the year in obtaining optical imagery. The Caribbean happens to be one such affected territory that is in need of national digital topographic information for its GIS database developments, 3D visualization of landscapes and for use in the digital ortho-rectification of satellite imagery. The use of Synthetic Aperture Radar (SAR), with its cloud penetrating and day/night imaging capabilities, is emerging as a possible remote sensing tool for use in cloud affected territories. There has been success with airborne single-pass dual antennae systems (e.g. STAR 3i) and the Shuttle Radar Topographic Mapping (SRTM) mission. However, the use of these systems in the Caribbean are restrictive and datasets will not be generally available. The launching of imaging radar satellites such as ERS-1, ERS-2, Radarsat-1 and more recently Envisat have provided additional opportunities for augmenting the technologies available for generating medium accuracy, low cost, topographic information for developing countries by using the techniques of Radargrammetry (StereoSAR) and Interferometric SAR (InSAR). The primary aim of this research was to develop, from scratch, a prototype StereoSAR system based on automatic stereo matching and space intersection algorithms to generate medium accuracy, low cost DSMs, using various influencing parameters without any recourse to ground control points. The result was to be a software package to undertake this process for implementation on a personal computer. The DSMs generated from Radarsat-1 and Envisat SAR imagery were compared with a reference surface from airborne InSAR and conclusions with respect to the quality of the StereoSAR DSMs are presented. Work required to further improve the StereoSAR system is also suggested

    Digital Surface Modelling in Developing Countries Using Spaceborne SAR Techniques

    Get PDF
    Topographic databases at the national level, in the form of Digital Surface Models (DSMs), are required for a large number of applications which have been spurred on by the increased use of Geographic Information Systems (GIS). Ground-Based (surveying, GPS, etc.) and traditional airborne approaches to generating topographic information are proving to be time consuming and costly for applications in developing countries. Where these countries are located in the tropical zone, they are affected by the additional problem of cloud cover which could cause delays for almost 75% of the year in obtaining optical imagery. The Caribbean happens to be one such affected territory that is in need of national digital topographic information for its GIS database developments, 3D visualization of landscapes and for use in the digital ortho-rectification of satellite imagery. The use of Synthetic Aperture Radar (SAR), with its cloud penetrating and day/night imaging capabilities, is emerging as a possible remote sensing tool for use in cloud affected territories. There has been success with airborne single-pass dual antennae systems (e.g. STAR 3i) and the Shuttle Radar Topographic Mapping (SRTM) mission. However, the use of these systems in the Caribbean are restrictive and datasets will not be generally available. The launching of imaging radar satellites such as ERS-1, ERS-2, Radarsat-1 and more recently Envisat have provided additional opportunities for augmenting the technologies available for generating medium accuracy, low cost, topographic information for developing countries by using the techniques of Radargrammetry (StereoSAR) and Interferometric SAR (InSAR). The primary aim of this research was to develop, from scratch, a prototype StereoSAR system based on automatic stereo matching and space intersection algorithms to generate medium accuracy, low cost DSMs, using various influencing parameters without any recourse to ground control points. The result was to be a software package to undertake this process for implementation on a personal computer. The DSMs generated from Radarsat-1 and Envisat SAR imagery were compared with a reference surface from airborne InSAR and conclusions with respect to the quality of the StereoSAR DSMs are presented. Work required to further improve the StereoSAR system is also suggested
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