A robust nonlinear scale space change detection approach for SAR images

In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling (FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction and shape detail preservation. MSERs of each scale space image are found and then combined through a decision level fusion strategy, namely "selective scale fusion" (SSF), where contrast and boundary curvature of each MSER are considered. The performance of the proposed method is evaluated using real multitemporal high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change. One of the main outcomes of this approach is that different objects having different sizes and levels of contrast with their surroundings appear as stable regions at different scale space images thus the fusion of results from scale space images yields a good overall performance

A Novel Algorithm for Ship Detection in SAR Imagery Based on the Wavelet Transform

Carrying out an effective control of fishing activities is essential to guarantee a sustainable exploitation of sea resources. Nevertheless, as the regulated areas are extended, they are difficult and time consuming to monitor by means of traditional reconnaissance methods such as planes and patrol vessels. On the contrary, satellite-based synthetic aperture radar (SAR) provides a powerful surveillance capability allowing the observation of broad expanses, independently from weather effects and from the day and night cycle. Unfortunately, the automatic interpretation of SAR images is often complicated, even though undetected targets are sometimes visible by eye. Attending to these particular circumstances, a novel approach for ship detection is proposed based on the analysis of SAR images by means of the discrete wavelet transform. The exposed method takes advantage of the difference of statistical behavior among the ships and the surrounding sea, interpreting the information through the wavelet coefficients in order to provide a more reliable detection. The analysis of the detection performance over both simulated and real images confirms the robustness of the proposed algorithm.Peer Reviewe

Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data

Multiscale segmentation of SAR imagery

Caption title.Includes bibliographical references (p. [11]).Sponsored in part by Advanced Research Projects Agency under Air Force Contract. F19628-95-C-0002 Sponsored in part by Air Force Office of Scientific Research Grant. F49620-93-1-0604, F49620-95-1-0083C.H. Fosgate ... [et al.]

Local isotropy indicator for SAR image filtering: application to Envisat/ASAR images of the Doñana Wetland

©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper explores a geometrical and computationally simple operator, named Ds, for local isotropy assessment on SAR images. It is assumed that isotropic intensity distributions in natural areas, either textured or nontextured, correspond to a single cover class. Ds is used to measure isotropy in processing neighborhoods and decide if they can be considered as belonging to a unique cover class. The speckle statistical properties are used to determine suitable Ds thresholds for discriminating heterogeneous targets from isotropic cover types at different window sizes. An assessment of Ds as an edge detector showed sensitivities similar to those of the ratio edge operator for straight, sharp boundaries, centered in the processing window, but significantly better sensitivity for detecting heterogeneities during the window expansion in multiresolution filtering. Furthermore, Ds presents the advantage versus the ratio edge coefficient of being rotationally invariant, and its computation indicates the direction of the main intensity gradient in the processing window. The Ds operator is used in a multiresolution fashion for filtering ASAR scenes of the Doñana wetland. The intensities in isotropic areas are averaged in order to flatten fluctuations within cover types and facilitate a subsequent land cover classification. The results show high degree of smoothing within textured cover classes, plus effective spatial adaptation to gradients and irregular boundaries, substantiating the usefulness of this operator for filtering SAR data of natural areas with the purpose of classification.Peer ReviewedPostprint (author's final draft

A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.Comment: 8 pages, 9 figures, International Conference for Data Intelligence and Security (ICDIS

Automatic and semi-automatic extraction of curvilinear features from SAR images

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images

ERS SAR imagery for urban climate studies

International audienceThis study investigates the potentialities of ERS SAR imagery for the urban micro-climate and air quality, over the city of Nantes, France. The temporal variability of the SAR signal over the city has been assessed by analyzing five images in descending mode. Beside the speckle effect, the quality of the signal is highly variable from one image to the other. Meteorological effects. make contrasts between objects and their surroundings to be more or less pronounced. Urban features are mostly present in each image, but the structures are not always well perceived within a single image. It is concluded that it is necessary to have several images. Their redundancy allows a better exploitation of the urban features. Further it decreases the level of speckle. Screening of the average SAR image clearly indicates that the perception of the roads is highly dependent on the flight direction of the spacecraft. The main factors for the perception of the morphological features are the height of the buildings, its orientation relative to the spacecraft orbit, its horizontal surface, its materials. Multiresolution analysis, by means of wavelet transform or structure function, provides a good discrimination between unbuilt areas, residential areas, industrial areas, and large groups of buildings. This preliminary study has demonstrated that urban morphological features and their typologies with relation to the air flow drag were well-perceived in SAR imagery once properly processed. Further studies are required to assess definitely the benefits and the limits of such images in urban micro-climate and air quality

Optical Propagation and Communication

Contains an introduction and reports on three research projects.Maryland Procurement Office Contract MDA 903-94-C6071Maryland Procurement Office Contract MDA 904-93-C4169U.S. Air Force - Office of Scientific Research Grant F49620-93-1-0604U.S. Air Force - Office of Scientific Research Grant F49620-96-1-0028U.S. Army Research Office Grant DAAHO4-95-1-0494U.S. Air Force - Office of Scientific Research Grant F49620-96-1-0126U.S. Army Research Office Grant DAAHO4-93-G-018