12,537 research outputs found
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
Investigating SAR algorithm for spaceborne interferometric oil spill detection
The environmental damages and recovery of terrestrial ecosystems from oil spills can last decades. Oil spills have been responsible for loss of aquamarine lives, organisms, trees, vegetation, birds and wildlife. Although there are several methods through which oil spills can be detected, it can be argued that remote sensing via the use of spaceborne platforms provides enormous benefits. This paper will provide more efficient means and methods that can assist in improving oil spill responses. The objective of this research is to develop a signal processing algorithm that can be used for detecting oil spills using spaceborne SAR interferometry (InSAR) data. To this end, a pendulum formation of multistatic smallSAR carrying platforms in a near equatorial orbit is described. The characteristic parameters such as the effects of incidence angles on radar backscatter, which support the detection of oil spills, will be the main drivers for determining the relative positions of the small satellites in formation. The orbit design and baseline distances between each spaceborne SAR platform will also be discussed. Furthermore, results from previous analysis on coverage assessment and revisit time shall be highlighted. Finally, an evaluation of automatic algorithm techniques for oil spill detection in SAR images will be conducted and results presented. The framework for the automatic algorithm considered consists of three major steps. The segmentation stage, where techniques that suggest the use of thresholding for dark spot segmentation within the captured InSAR image scene is conducted. The feature extraction stage involves the geometry and shape of the segmented region where elongation of the oil slick is considered an important feature and a function of the width and the length of the oil slick. For the classification stage, where the major objective is to distinguish oil spills from look-alikes, a Mahalanobis classifier will be used to estimate the probability of the extracted features being oil spills. The validation process of the algorithm will be conducted by using NASA’s UAVSAR data obtained over the Gulf of coast oil spill and RADARSAT-1 dat
Analysis of GLCM Parameters for Textures Classification on UMD Database Images
Texture analysis is one of the most important techniques that have been used in image processing for many purposes, including image classification. The texture determines the region of a given gray level image, and reflects its relevant information. Several methods of analysis have been invented and developed to deal with texture in recent years, and each one has its own method of extracting features from the texture. These methods can be divided into two main approaches: statistical methods and processing methods. Gray Level Co-occurrence Matrix (GLCM) is the most popular statistical method used to get features from the texture. In addition to GLCM, a number of equations of Haralick characteristics will be used to calculate values used as discriminate features among different images in this study. There are many parameters of GLCM that should be taken into consideration to increase the discrimination between images belonging to different classes. In this study, we aim to evaluate GLCM parameters. For three decades now, GLCM is popular method used for texture analysis. Neural network which is one of supervised methods will also be used as a classifier. And finally, the database for this study will be images prepared from UMD (University of Maryland database)
FIREX mission requirements document for nonrenewable resources
The proposed mission requirements and a proposed experimental program for satellite synthetic aperture radar (SAR) system named FIREX (Free-Flying Imaging Radar Experiment) for nonrenewable resources is described. The recommended spacecraft minimum SAR system is a C-band imager operating in four modes: (1) low look angle HH-polarized; (2) intermediate look angle, HH-polarized; (3) intermediate look angle, IIV-polarized; and (4) high look angle HH-polarized. This SAR system is complementary to other future spaceborne imagers such as the Thematic Mapper on LANDSAT-D. A near term aircraft SAR based research program is outlined which addresses specific mission design issues such as preferred incidence angles or polarizations for geologic targets of interest
Joint space aspect reconstruction of wide-angle SAR exploiting sparsity
In this paper we present an algorithm for wide-angle synthetic aperture radar (SAR) image formation. Reconstruction of wide-angle SAR holds a promise of higher resolution and better information about a scene, but it also poses a number of challenges when compared to the traditional narrow-angle SAR. Most prominently, the isotropic point scattering model is no longer valid. We present an algorithm capable of producing high resolution reflectivity maps in both space and aspect, thus accounting for the anisotropic scattering behavior of targets. We pose the problem as a non-parametric three-dimensional inversion problem, with two constraints: magnitudes of the backscattered power are highly correlated across closely spaced look angles and the backscattered power
originates from a small set of point scatterers. This approach considers jointly all scatterers in the scene across all azimuths, and exploits the sparsity of the underlying scattering field. We implement the algorithm and present
reconstruction results on realistic data obtained from the XPatch Backhoe dataset
Region-enhanced passive radar imaging
The authors adapt and apply a recently-developed region-enhanced synthetic aperture radar (SAR) image reconstruction technique to the problem of passive radar imaging. One goal in passive radar imaging is to form images of aircraft using signals transmitted by commercial radio and television stations that are reflected from the objects of interest. This involves reconstructing an image from sparse samples of its Fourier transform. Owing to the sparse nature of the aperture, a conventional image formation approach based on direct Fourier transformation results in quite dramatic artefacts in the image, as compared with the case of active SAR imaging. The regionenhanced image formation method considered is based on an explicit mathematical model of the observation process; hence, information about the nature of the aperture is explicitly taken into account in image formation. Furthermore, this framework allows the incorporation of prior information or constraints about the scene being imaged, which makes it possible to compensate for the limitations of the sparse apertures involved in passive radar imaging. As a result, conventional imaging artefacts, such as sidelobes, can be alleviated. Experimental results using data based on electromagnetic simulations demonstrate that this is a promising strategy for passive radar imaging, exhibiting significant suppression of artefacts, preservation of imaged object features, and robustness to measurement noise
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