Image Analysis and Computer Vision

A Synthetic Aperture Radar (SAR) is an imaging sensor capable of capturing high-resolution aerial images under a variety of imaging conditions. SAR images find application in remote sensing and military target detection and surveillance. Since SAR images exhibit considerable variations in signal strength, even when imaging similar features or objects belonging to the same class, probabilistic descriptions are useful for modeling SAR data. This paper includes an overview of popular statistical distributions used to model real, complex, and polarimetric SAR images. Specialized techniques are necessary for analyzing SAR images due to their unique characteristics when compared to aerial images produced by other sensors. We focus on two distinct methods of SAR image analysis in this paper: Constant false alarm rate processing for target detections and pixel classification using statistical models. Previous work done in each of these areas is reviewed and compared. Some of the popular image analysis techniques are illustrated with experimental results from real high-resolution SAR data.

Statistical modeling and analysisof high-resolution Synthetic Aperture Radar images

Knowledge-based control of vision systems

This paper discusses algorithms that are useful for the classification of targets in SAR imagery. Two algorithms are presented for segmenting a target region from background clutter; one based on constant false alarm rate detection, and another histogram based technique. The histogram based technique is extended to extract shadow regions associated with a target. A method is then presented for estimating the orientation of segmented targets. These algorithms are applied to SAR imagery from the Lincoln Lab ADTS and MSTAR datasets. The aspect estimate is shown to be superior to estimates obtained from the direction of the axis of least inertia.

Target/shadow segmentation and aspect estimation in synthetic aperture radar imagery

Wide area site models are useful for delineating regions of interest and assisting in tasks like monitoring and change detection. They are also useful in registering a newly acquired image to an existing one of the same site, or to a map. This paper presents an algorithm for building a 2D wide area site model from high resolution, single polarization synthetic aperture radar (SAR) data. A three stage algorithm, involving detection of bright pixels, statistical segmentation of the data into homogeneous regions, and labeling/validation of segmentation results, is used for this task. Constant false alarm rate (CFAR) detectors are used for detecting bright pixels. Under assumptions of a suitable model for the statistical distribution of single polarization intensity or complex data, maximum likelihood labeling is used for initial segmentation. Knowledge of the acquisition parameters and other geometric cues are used to refine the initial segmentation and to extract man-made objects like buildings, and their shadows, as well as roads, from these images. When data from multiple passes of the same site is available, site models yield feature points which can be used to register the different images. In case complete information regarding the radar location, heading, and depression angle are available, the multiple views can be registered prior to site model construction, leading to improved performance. Site models are also useful for SAR data compression, where possible targets, man-made objects, and their neighborhoods are compressed losslessly and the background regions are compressed using lossy schemes.

Building 2D wide-area site models from single- and multipass single-polarization SAR data

Wide area site models are useful for delineating regions of interest and assisting in tasks like monitoring and change detection They are also useful in registering a newly acquired image to an existing one of the same site, or to a map This paper presents a complete algorithm for building an approximate 2-D wide-area site model from high resolution, polarimetric Synthetic Aperture Radar (SAR) data A three stage algorithm-involving detection of possible targets, statistical segmentation of the data into homogeneous regions, and validation of segmentation results-is used for this task Constant False Alarm Rate (CFAR) detectors are used for target detection, while maximum likelihood labeling is used for initial segmentation Knowledge of the sensor heading and other geometric cues are used to refine the initial segmentation and to extract man-made objects like buildings, and their shadows, as well as roads, from these images

http://ieeexplore.ieee.org/iel2/3465/10211/00477033.pdf

S. Kuttikkad

Papers

Building wide area 2-D site models from high resolution fully polarimetric synthetic aperture radar images