A multi-family GLRT for detection in polarimetric SAR images

This paper deals with detection from multipolarization SAR images. The problem is cast in terms of a composite hypothesis test aimed at discriminating between the Polarimetric Covariance Matrix (PCM) equality (absence of target in the tested region) and the situation where the region under test exhibits a PCM with at least an ordered eigenvalue smaller than that of a reference covariance. This last setup reflects the physical condition where the back scattering associated with the target leads to a signal, in some eigen-directions, weaker than the one gathered from a reference area where it is apriori known the absence of targets. A Multi-family Generalized Likelihood Ratio Test (MGLRT) approach is pursued to come up with an adaptive detector ensuring the Constant False Alarm Rate (CFAR) property. At the analysis stage, the behaviour of the new architecture is investigated in comparison with a benchmark (but non-implementable) and some other adaptive sub-optimum detectors available in open literature. The study, conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach

Model Order Selection Rules For Covariance Structure Classification

The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules

Detecting covariance symmetries for classification of polarimetric SAR images

The availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection and/or recognition capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, coherence matrix, Muller matrix, and to exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric SAR images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image

A multi-family GLRT-based algorithm for oil spill detection

This paper deals with detection of oil spills from multi-polarization SAR images. The problem is cast in terms of a composite hypothesis test aimed at discriminating between the Polarimetric Covariance Matrix (PCM) equality (absence of oil spills in the tested region) and the situation where the region under test exhibits a PCM with at least an ordered eigenvalue smaller than that of a reference covariance. This last setup reflects the physical condition where the back scattering associated with the oil spills leads to a signal, in some eigen-directions, weaker than the one gathered from a reference area where it is a-priori known the absence of any oil slicks. A Multi-family Generalized Likelihood Ratio Test (MGLRT) approach is pursued to come up with an adaptive detector ensuring the Constant Alarm False Rate (CFAR) property. At the analysis stage, the behavior of the new architecture is investigated in comparison with a benchmark (but non-implementable) structure and some other sub-optimum adaptive detectors available in open literature. The study, conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach

Classification of covariance matrix eigenvalues in polarimetric SAR for environmental monitoring applications

In this paper, we describe novel techniques for automatic classification of the dominant scattering mechanisms associated with the pixels of polarimetric SAR images. Specifically, we investigate two operating scenarios. In the first scenario, it is assumed that the polarimetric image pixels locally share the same covariance (homogeneous environment), whereas the second scenario considers polarimetric pixels with different power levels and the same covariance structure (heterogeneous environment). In the second case, we invoke the Principle of Invariance to get rid of the dependence on the power levels. For both scenarios, we formulate the classification problem in terms of multiple hypothesis tests which is addressed by applying the model order selection rules. The performance analysis is conducted on both simulated and measured data and demonstrates the effectiveness of the proposed approach

The multifamily likelihood ratio test for multiple signal model detection

The generalized likelihood ratio test (GLRT) is a standard tool for designing signal detectors. It produces detectors that perform well when the signal model is known, except for a fixed set of signal parameters. However, it is unable to accommodate multiple signal models. We introduce the multifamily likelihood ratio test, which extends the GLRT and alleviates this limitation. © 2005 IEEE

Imaging, Detection, and Identification Algorithms for Position-Sensitive Gamma-Ray Detectors.

Three-dimensional-position-sensitive semiconductors record both the locations and energies of gamma-ray interactions with high resolution, enabling spectroscopy and imaging of gamma-ray-emitting materials. Imaging enables the detection of point sources of gamma rays in an otherwise extended-source background, even when the background spectrum is unknown and may share the point source's spectrum. The generalized likelihood ratio test (GLRT) and source-intensity test (SIT) are applied to this situation to detect one-or-more unshielded point sources from a library of isotopes in a spectrally unknown or known background when the background intensity varies spatially by a factor of two or less. In addition to estimating the number of sources present, their activities, isotopes, and directions from the detector are estimated. Experimental and some simulated results are presented for a single detector and an 18-detector array of 2 cm by 2 cm by 1.5 cm CdZnTe crystals and compared with the performance of spectral-only detection when the background and source are assumed to be spectrally different. Furthermore, the expected detection performance of the 18-detector array system is investigated statistically using experimental data in the case where the background is distinct spectrally from the point source and the possible source location and isotopic identity are known. Including imaging gave at least 7% higher SNR compared to ignoring the image dimension. Also, imaging methods based on the maximum-likelihood expectation-maximization method are introduced to determine the spatial distribution of isotopes and to find the activity distributions within targets moving with known motion through a radioactive background. Software has also been developed to support the analysis of the data from 3D-position-sensitive spectroscopic systems, for a range of detector designs and applications. The software design and unique features that allow fast multidimensional data analysis are presented, along with parallel computing performance.Ph.D.Nuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89797/1/cgwahl_1.pd