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This paper deals with signal reconstruction for multichannel high-resolution wide-swath synthetic aperture radar (HRWS-SAR) systems. One of the commonly used algorithms for processing HRWS-SAR data is Krieger's digital beamforming (DBF) algorithm. This algorithm works quite well for cases with uniform space–time sampling as well as moderately nonuniform sampling. However, for cases with highly nonuniform sampling, Krieger's DBF algorithm in its original form suffers from a severe degradation in the signal-to-noise ratio, and for cases with coinciding sampling, this algorithm even fails to function. In this paper, we will solve these problems such that an improved DBF (IDBF) algorithm is developed. The key of the IDBF algorithm consists of two procedures: The first one implements an operation called ambiguity-index screening, which is based on two novel quantities, termed the equivalent sampling spacing and the effective reconstructed Doppler bandwidth; and the other key procedure implements a Doppler-spectrum weighting based on a newly defined parameter that is termed the maximum-to-minimum ratio of the noise spectrum. Simulation results show that the newly proposed IDBF algorithm shares a pronounced improvement in the imaging performance over Krieger's DBF algorithm. Moreover, simulation results also demonstrate that the IDBF algorithm exhibits a more robust and consistent overall performance than several other existing reconstruction algorithms. [ABSTRACT FROM PUBLISHER]

The frequency scaling algorithm (FSA) was proposed to process the synthetic aperture radar (SAR) data acquired via the dechirp-on-receive approach. Some aspects of improving the FSA are investigated in this paper, based on which an extended FSA (EFSA) is presented. The general purpose of the EFSA is to reduce the effect of range spectrum shift of the intermediate processing results, which occurs during the scaling operation in the FSA, so as to achieve a more effective utilization of the processed bandwidth. The EFSA is implemented through time shifting the scaling and the inverse scaling functions used in the FSA and also the adjustment of the scaling factor. The derivation of the EFSA is detailed in this paper. Point target simulation in squinted imaging geometry indicates that the presented algorithm is more suitable for large-squint applications. [ABSTRACT FROM AUTHOR]

In this paper, we address the theoretical aspects of coherence region modeling and its inversion methods for polarimetric interferometric synthetic aperture radar (PolInSAR). Instead of only considering the geometrical shape of the coherence region, we focus on directly modeling the whitened interferometric cross-correlation matrix of the PolInSAR data. In particular, we consider three classes of generic scattering models that respectively contain single, double, and triple phase centers within one resolution cell. We then demonstrate, for each class, that the modeled whitened interferometric cross-correlation matrix is normal. Based on this property, we propose, for each case, efficient algorithms to obtain the best normal matrix approximation solution for model inversion from the observed whitened interferometric cross-correlation matrix. In addition, we show how a simple and effective criterion can be designed from the derived solutions for model validation. Finally, we verify the solutions with both simulated and real data. The results prove the proposed method to be a very promising tool for PolInSAR applications. [ABSTRACT FROM PUBLISHER]

A linear frequency-modulated continuous-wave (LFMCW) synthetic aperture radar (SAR) is a highly miniaturized SAR that is suitable for use on a small unmanned aerial vehicle. In squint stripmap mode, the LFMCW SAR image quality is degraded by the presence of nonlinearities in the transmitted signal, combined with the large bandwidth introduced by the frequency scaling in the nonlinear frequency scaling (NLFS) algorithm. This paper proposes an extension of the NLFS algorithm called the Doppler-centroid-dependent frequency scaling algorithm. The algorithm introduces a new Doppler-centroid-dependent scaling factor to decrease the introduced bandwidth, effectively removing the range frequency aliasing, without extra computational load. Nonlinearity correction is performed at the same time as range cell migration correction. The validity of the proposed algorithm is demonstrated with point target simulation results. [ABSTRACT FROM AUTHOR]

In this paper, heterogeneous clutter models are used to describe polarimetric synthetic aperture radar (PolSAR) data. The KummerU distribution is introduced to model the PolSAR clutter. Then, a detailed analysis is carried out to evaluate the potential of this new multivariate distribution. It is implemented in a hierarchical maximum likelihood segmentation algorithm. The segmentation results are shown on both synthetic and high-resolution PolSAR data at the X- and L-bands. Finally, some methods are examined to determine automatically the “optimal” number of segments in the final partition. [ABSTRACT FROM AUTHOR]

It is important to monitor environmental changes of the Earth's cover by remotely sensed data. This paper analyzes seasonal changes of a wetland by a modified polarimetric four-component scattering power decomposition method. The data sets analyzed here are L- and X-band fully polarimetric synthetic aperture radar (POLSAR) data, which have been acquired by the NICT/JAXA airborne polarimetric and interferometric synthetic aperture radar system in 2004. Since there existed a deficiency in the currently adopted decomposition schemes in that negative powers appear in a few pixels in the image analysis, we modified the approach taking into account physical conditions. It is shown by the modified scheme that the seasonal changes and features of the vegetation near Sakata Lagoon in Niigata, Japan, are observed clearly, demonstrating the utility of POLSAR image analysis for wetland assessments in general. [ABSTRACT FROM AUTHOR]

We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images Over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (κ, Weibull, Log-normal, Nakaganhi-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolutions The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground; buildings, and vegetation. [ABSTRACT FROM AUTHOR]

Multiobjective segmentation algorithms are based on an objective function, consisting of two or more terms, that is minimized by using an optimization algorithm. The objective terms represent differing segmentation objectives, the most popular of which are statistical likelihood of pixel values and smoothness of segment boundaries. Many assumptions are built into the objective function, and we present a case study based on the algorithm of Stewart et al. to demonstrate the importance of analyzing algorithm characteristics to test the validity of hidden assumptions. We develop a set of simulated test images and a novel segmentation performance metric for use with simulated data. An innovative aspect of the Stewart algorithm (SA) is the probability of false alarm (PFA) model used to weight the objective terms. This is intended to dynamically balance the terms as the algorithm progresses. The PFA model is only valid for false edges, and we have shown that the number of selected true edges increases as segmentation evolves, making the theoretical weight model increasingly invalid. In addition, we found problems with several other algorithm assumptions. We tested algorithm performance against a fixed-weight version. We found that the performance of the SA was worse than a fixed-weight version. Thus, while the two-term objective function algorithm does deliver reasonable performance for multilook data, the fixed-weight version gives better performance. While these results hold only for simulated data, we believe that the experimental results indicate the need for a more powerful approach to multiobjective synthetic aperture radar segmentation. [ABSTRACT FROM AUTHOR]