Angel Caroline Johnsy

A Fast Support Detector for Superresolution Localization of Multiple Scatterers in SAR Tomography

This paper is focused on the problem of the detection of multiple scatterers in synthetic aperture radar (SAR) tomography. The method presented exploits the a priori information that at most

Speckle reduction based on Wiener filter in ultrasound images

K_{{\rm{max}}}

Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas

different scatterers are present in the same range-azimuth resolution cell. In particular, a simplified version of a generalized-likelihood ratio test (GLRT) detector, based on support estimation (Sup-GLRT), is proposed. The Sup-GLRT is a constant false alarm rate sequential test that detects the presence of scatterers, one after another, and estimates their positions, detecting the support of the unknown signal. The proposed simplified test denoted as Fast-Sup-GLRT detector, despite still being a multistep statistical hypothesis test, exploits, at each step i, an approximated maximum-likelihood estimate of the signal support of cardinality i−1, based on the sequential estimation of i −1 supports of cardinality one. The introduced approximation allows a considerable reduction of the computational complexity, which from the combinatorial trend of Sup-GLRT passes to the linear one of Fast-Sup-GLRT, without significantly impairing the detection probability. The performance of the proposed approach is analyzed using TerraSAR-X system parameters, with particular reference to the elevation superresolution achievable for an assigned probability of false alarm and with a given number of acquisitions. Numerical results on simulated and real data are presented and discussed.

A modified statistical test based on support estimation for multiple scatterers detection in SAR tomography

In this manuscript, a technique for speckle noise reduction in ultrasound images is presented. The method exploits Wiener filter and is able to take into account spatial correlation among noise samples. With respect to classical Wiener filter approach developed in independence hypothesis, the methodology is able to sensibly improve filtering performances, at the cost of no computational time increase. Results on realistic simulated datasets are reported, showing the effectiveness of the approach.

A Bayesian method for speckle reduction in single-look SAR images

This paper analyzes a method for Synthetic Aperture Radar (SAR) Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers that can exhibit time deformation and thermal dilation by using a CFAR (Constant False Alarm Rate) approach. In the last decade, several methods for TomoSAR have been proposed. The objective of this paper is to present the results obtained on high resolution tomographic SAR data of urban areas, by using a statistical test for detecting multiple scatterers that takes into account phase variations due to possible deformations and/or thermal dilation. The test can be evaluated in terms of probability of detection (PD) and probability of false alarm (PFA), and is based on an approximation of a Generalized Likelihood Ratio Test (GLRT), denoted as Fast-Sup-GLRT. It was already applied and validated by the authors in the 3D case, while here it is extended and experimented in the 5D case. Numerical experiments on simulated and on StripMap TerraSAR-X (TSX) data have been carried out. The presented results show that the adopted method allows the detection of a large number of scatterers and the estimation of their position with a good accuracy, and that the consideration of the thermal dilation and surface deformation helps in recovering more single and double scatterers, with respect to the case in which these contributions are not taken into account. Moreover, the capability of method to provide reliable estimates of the deformations in urban structure suggests its use in structure stress monitoring.

Urban SAR image filtering exploiting Bayesian estimation theory

Detection of multiple scatterers for localizing the targets is one of the key issues in SAR tomography. Recently, a Generalized Likelihood Ratio Test based on support estimation (Sup-GLRT) [10] has been presented. This test exhibits a high computational complexity. In this paper a modified approach for reducing computational complexity (Fast-Sup-GLRT) is proposed. The prime objective is to analyze the performance of Fast-Sup-GLRT detector in terms of implementation and computational complexity. For an assigned probability of false alarm and with a given number of acquisitions the performance is analyzed and compared with the one obtained with the Sup-GLRT. Results on simulated and real HighRes SpotLight TerraSAR-X data are presented.

A lossless coding scheme for maps using binary wavelet transform

In this paper the problem of despeckling Synthetic Aperture Radar images is addressed. An algorithm developed in the Bayesian estimation theory framework is presented. In particular, considering single look images, the algorithm applies an homomorphic filter followed by an Iterative Wiener filter to reduce the speckle. The proposed approach is tested on simulated and real X-band datasets showing interesting noise reduction capabilities.

Support-detection 5-D SAR tomography

In this manuscript, a technique based on Bayesian estimation theory for filtering Synthetic Aperture Radar images is presented. The technique applies a Wiener filter to the available data, after an homomorphic transformation. With respect to classical a Wiener filter approach, the algorithm has two main advantages: it is able to take into account the spatial correlation among noise samples and it is able to automatically adapt the filter behavior to the image characteristics. This allows an improvement of the filter accuracy, without increasing the algorithm complexity: the good performances in terms of time consuming of Wiener filter are well preserved. Results on real datasets are reported and compared to the ones achievable using other filters existing in literature, showing the effectiveness of the approach.

SAR despeckling based on Enhanced Wiener Filter

ABSTRACT The maps and images of geographical information system (GIS) are used for finding locations, accessing rail, bus routes, and for educational purposes such as study on vegetation, landscapes, population, and so on and so forth. Remote sensing is the process of acquiring data about Earth by using satellites or satellite-borne or airborne sensors. The images acquired through remote sensing systems are integrated within GIS to store, analyze, and manipulate geographical information of the Earth. The huge size of the digital raster maps makes compression inevitable in particular to reduce the transmission time and display them on the Internet as well as other networks. In this paper, a lossless coding approach that performs encoding on the decomposed binary layers by taking the advantage of binary wavelet transform that produces sparse matrix for row column reduction and Huffman coding is presented. The results obtained on raster maps are compared with those of other existing techniques.

Support based multiple scatterers detection in SAR tomography

In this paper we extend the Fast-Sup-GLRT Detector, designed for SAR tomography (3D-SAR), to the detection of multiple scatterers that can exhibit time deformation. It assumes at most Kmax different scatterers in the same range-azimuth resolution cell with a phase model that takes into account phase variations due to the deformation and/or dilation of the scatterer(s). Results on simulated and real data are presented to validate the proposed approach.