1,720,988 research outputs found
A lossless coding scheme for maps using binary wavelet transform
Get PDFThe 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
Urban tomographic imaging using polarimetric SAR data
Get PDFIn this paper, we investigate the potential of polarimetric Synthetic Aperture Radar (SAR) tomography (Pol-TomoSAR) in urban applications. TomoSAR exploits the amplitude and phase of the received data and offers the possibility to resolve multiple scatters lying in the same range-azimuth resolution cell. In urban environments, this issue is very important since layover causes multiple coherent scatterers to be mapped in the same range-azimuth image pixel. To achieve reliable and accurate results, TomoSAR requires a large number of multi-baseline acquisitions which, for satellite-borne SAR systems, are collected with long time intervals. Then, accurate tomographic reconstructions would require multiple scatterers to remain stable between all the acquisitions. In this paper, an extension of a generalized likelihood ratio test (GLRT)-based tomographic approach, denoted as Fast-Sup-GLRT, to the polarimetric data case is introduced, with the purpose of investigating if, in urban applications, the use of polarimetric channels allows for reduction of the number of baselines required to achieve a given scatterer's detection performance. The results presented show that the use of dual polarization data allows the proposed detector to work in an equivalent or better way than use of a double number of independent single polarization channels
Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas
Get PDFThis 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
A Fast Support Detector for Superresolution Localization of Multiple Scatterers in SAR Tomography
No full textThis 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 Kmax 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
No full textDetection cof 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
Support based multiple scatterers detection in SAR tomography
No full textIn this paper we focus on the detection of single and double scatterers in SAR tomography. In particular, the performance of a support based Generalized Likelihood Ratio Test (GLRT) approach is analyzed, using TerraSAR-X system parameters, with particular reference to the elevation resolution achievable for an assigned probability of false alarm and with a given number of acquisitions. Results on simulated and real data are presented
Support-detection 5-D SAR tomography
No full textIn 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
4-D SAR support based tomographic imaging
No full textSAR Tomographic techniques have been successfully used for separating the contribution of multiple coherent scatterers lying in the same range azimuth resolution cell, but usually they assume stationary scatterers and does not account for phase variations due to time deformation and/or dilation. In this paper we investigate the design of a detector in SAR tomography for multiple scatterers that can exhibit a time deformation. It exploits the a priori information that at most Kmax different scatterers are present in the same range-Azimuth resolution cell and a phase model that allows modelling time deformation, thereby at the same time, the detector allows a simultaneous retrieval of at most Kmax scatterers with their elevation and deformation parameters
Sparsity based TomoSAR combining CS and GLRT
No full textIn this paper a new approach to TomoSAR imaging is presented. It is based on the joint use of a Constant False Alarm Rate (CFAR) detection approach of multiple targets and of Compressive Sampling (CS) tomographic reconstructions. CS is widely used to recover a sparse signal but suffers from the presence of outliers. The proposed method consists in applying a Generalized Likelihood Ratio Test (GLRT) exploiting the CS reconstruction in order to detect and accurately localize single and double scatterers with a given false alarm probability, avoiding outliers and artefacts
- …
