Statistical Modeling of SAR Images: A Survey

Statistical modeling is essential to SAR (Synthetic Aperture Radar) image interpretation. It aims to describe SAR images through statistical methods and reveal the characteristics of these images. Moreover, statistical modeling can provide a technical support for a comprehensive understanding of terrain scattering mechanism, which helps to develop algorithms for effective image interpretation and creditable image simulation. Numerous statistical models have been developed to describe SAR image data, and the purpose of this paper is to categorize and evaluate these models. We first summarize the development history and the current researching state of statistical modeling, then different SAR image models developed from the product model are mainly discussed in detail. Relevant issues are also discussed. Several promising directions for future research are concluded at last

Optical Theory Improvements to Space Domain Awareness

This dissertation focuses on increasing the ability to detect space objects and increase Space Domain Awareness (SDA) with space surveillance sensors through image processing and optical theory. SDA observations are collected through ground-based radar and optical systems as well as space based assets. This research focuses on a ground-based optical telescope system, the Space Surveillance Telescope (SST). By increasing the number of detectable Resident Space Objects (RSOs) through image processing, SDA capabilities can be expanded. This is accomplished through addressing two main degrading factors present in typical SDA sensors; spatial undersampling in the collected data and noise models and assumptions used in current algorithms. The assigned cost and a priori probabilities of a Bayes Multiple Hypothesis Test (MHT) are investigated in this dissertation to address the spatial undersampling. New algorithms are developed and tested, and demonstrated improved detection capabilities at operationally realistic false alarm rates. Additionally, a new noise model is developed which more accurately represents the received noise present in data collected with surveillance telescopes under certain atmospheric conditions. These algorithm have demonstrated probability of detection improvement of up to 80 percent in collected SST data over the currently employed detection techniques

Phase History Decomposition for Efficient Scatterer Classification in SAR Imagery

A new theory and algorithm for scatterer classification in SAR imagery is presented. The automated classification process is operationally efficient compared to existing image segmentation methods requiring human supervision. The algorithm reconstructs coarse resolution subimages from subdomains of the SAR phase history. It analyzes local peaks in the subimages to determine locations and geometric shapes of scatterers in the scene. Scatterer locations are indicated by the presence of a stable peak in all subimages for a given subaperture, while scatterer shapes are indicated by changes in pixel intensity. A new multi-peak model is developed from physical models of electromagnetic scattering to predict how pixel intensities behave for different scatterer shapes. The algorithm uses a least squares classifier to match observed pixel behavior to the model. Classification accuracy improves with increasing fractional bandwidth and is subject to the high-frequency and wide-aperture approximations of the multi-peak model. For superior computational efficiency, an integrated fast SAR imaging technique is developed to combine the coarse resolution subimages into a final SAR image having fine resolution. Finally, classification results are overlaid on the SAR image so that analysts can deduce the significance of the scatterer shape information within the image context