TEXTURE SEGMENTATION METHODS FOR SATELLITE RADAR IMAGES

Segmentation for Synthetic Aperture Radar (SAR) is a very important aspect for satellite radar images. It is important to separate areas that be clustered based on characteristics or features of the image. Nowadays, there have a lot of segmentation techniques of SAR images. In this thesis, the techniques being investigated are edge adaptive smoothing, watershed transform, mean shft segmentation and region merging via boundary melting techniques which is the best among segmentation techniques. The comparison or evalution among the techniques is in term of number of edges retained in the segmented images and also in visual inspection. In this research, we use two different type of images, which is real SAR image and non-real SAR image (house). Results generated from this research has shown that edge adaptive smoothing is the best one compared to the other segmentation techniques

Effective SAR sea ice image segmentation and touch floe separation using a combined multi-stage approach

Accurate sea-ice segmentation from satellite synthetic aperture radar (SAR) images plays an important role for understanding the interactions between sea-ice, ocean and atmosphere in the Arctic. Processing sea-ice SAR images are challenging due to poor spatial resolution and severe speckle noise. In this paper, we present a multi-stage method for the sea-ice SAR image segmentation, which includes edge-preserved filtering for pre-processing, k-means clustering for segmentation and conditional morphology filtering for post-processing. As such, the effect of noise has been suppressed and the under-segmented regions are successfully corrected

SAR image segmentation with GMMs

This paper proposes a new approach for Synthetic Aperture Radar (SAR) image segmentation. Segmenting SAR images can be challenging because of the blurry edges and the high speckle. The segmentation proposed is based on a machine learning technique. Gaussian Mixture Models (GMMs) were already used to segment images in the visual field and are here adapted to work with single channel SAR images. The segmentation suggested is designed to be a first step towards feature and model based classification. The recall rate is the most important as the goal is to retain most target's features. A high recall rate of 88%, higher than for other segmentation methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, was obtained. The next classification stage is thus not affected by a lack of information while its computation load drops. With this method, the inclusion of disruptive features in models of targets is limited, providing computationally lighter models and a speed up in further classification as the narrower segmented areas foster convergence of models and provide refined features to compare. This segmentation method is hence an asset to template, feature and model based classification methods. Besides this method, a comparison between variants of the GMMs segmentation and a classical segmentation is provided

Knowledge-based segmentation of SAR data with learned priors

©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/83.821747An approach for the segmentation of still and video synthetic aperture radar (SAR) images is described in this note. A priori knowledge about the objects present in the image, e.g., target, shadow, and background terrain, is introduced via Bayes' rule. Posterior probabilities obtained in this way are then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show with examples from public data sets that this method provides an efficient and fast technique for addressing the segmentation of SAR data

An Objective Evaluation of Four SAR Image Segmentation Algorithms

Because of the large number of SAR images the Air Force generates and the dwindling number of available human analysts, automated methods must be developed. A key step towards automated SAR image analysis is image segmentation. There are many segmentation algorithms, but they have not been tested on a common set of images, and there are no standard test methods. This thesis evaluates four SAR image segmentation algorithms by running them on a common set of data and objectively comparing them to each other and to human segmentors. This objective comparison uses a multi-metric a approach with a set of master segmentations as ground truth. The metric results are compared to a Human Threshold, which defines performance of human se mentors compared to the master segmentations. Also, methods that use the multi-metrics to determine the best algorithm are developed. These methods show that of the four algorithms, Statistical Curve Evolution produces the best segmentations; however, none of the algorithms are superior to human segmentors. Thus, with the Human Threshold and Statistical Curve Evolution as benchmarks, this thesis establishes a new and practical framework for testing SAR image segmentation algorithms

Quantitative assessment for detection and monitoring of coastline dynamics with temporal RADARSAT images

© 2018 by the authors. This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds

TEXTURE SEGMENTATION METHODS FOR SATELLITE RADAR IMAGES

Segmentation for Synthetic Aperture Radar (SAR) is a very important aspect for satellite radar images. It is important to separate areas that be clustered based on characteristics or features of the image. Nowadays, there have a lot of segmentation techniques of SAR images. In this thesis, the techniques being investigated are edge adaptive smoothing, watershed transform, mean shft segmentation and region merging via boundary melting techniques which is the best among segmentation techniques. The comparison or evalution among the techniques is in term of number of edges retained in the segmented images and also in visual inspection. In this research, we use two different type of images, which is real SAR image and non-real SAR image (house). Results generated from this research has shown that edge adaptive smoothing is the best one compared to the other segmentation techniques