A spatially selective approach to Doppler estimation for frame-based satellite SAR processing

Abstract

Abstract-When Doppler centroid estimators are applied to satellite synthetic aperture radar (SAR) data, biased estimates are often obtained because of anomalies in the received data. Typical anomalies include areas of low SNR, strong discrete targets, and radiometric discontinuities. In this paper, a new method of Doppler centroid estimation is presented that takes advantage of principles such as spatial diversity, estimator quality checks, geometric models, and the fitting of a "global" estimate over a wide area of a SAR scene. In the proposed scheme, Doppler estimates are made over small blocks of data covering a whole frame, so that all parts of the scene are potentially represented. The quality of each block estimate is examined using data statistics or estimator quality measures. Poor estimates are rejected, and the remaining estimates are used to fit a surface model of the Doppler centroid versus the range and azimuth extent of the scene. A physical model that relates the satellite's orbit, attitude, and beam-pointing-direction to the Doppler centroid is used to get realistic surface fits and to reduce the complexity (dimensionality) of the estimation problem. The method is tested with RADARSAT-1 and Shuttle Radar Topography Mission X-band SAR (SRTM/X-SAR) spaceborne data and is found to work well with scenes that do have radiometric anomalies, and in scenes where attitude adjustments cause the Doppler to change rapidly.Index Terms-Geometry models, global surface fit, quality metrics, satellite synthetic aperture radar (SAR) Doppler centroid estimation.

Adding Sensitivity to the MLBF Doppler Centroid Estimator

“…As shown in (1), the phase of a range-compressed signal in the range-frequency domain consists of a constant term −4πf 0 R(η)/c and a term −4πf τ R(η)/c that is linear with respect to the range frequency. The second term is important to the MLBF algorithm.…”

“…This approach is inspired by the "Spatial Diversity" concept [1], whose objective is to identify and isolate parts of the scene that are likely to lead to noisy or biased Doppler centroid estimates. In this paper, the range-compressed Vancouver data set is divided into 12 range × 19 azimuth blocks.…”

“…In the baseband Doppler centroid estimation, several algorithms, such as the "Spectral fit" and average crosscorrelation (ACCC) methods, give reliable estimates in most cases-usually better than 1% of the PRF [1]. In solving for the Doppler ambiguity number, several algorithms have been used, such as the look displacement [2], multiple PRF [3], wavelength diversity (WDA) [4], multilook cross correlation (MLCC) [5], and multilook beat frequency (MLBF) algorithms [5].…”

“…However, the accuracy and robustness of the Doppler ambiguity number estimate still need improvement to satisfy current high-quality SAR processing requirements. 1 The MLBF algorithm proposed in 1996 is the most reliable of the current ways of estimating the Doppler ambiguity of SAR signals. The method is based on extracting two looks from the data in the range-frequency domain, beating (conjugate multiplying) the two looks together, then estimating the center frequency of the beat signal.…”

Improved slope estimation for SAR Doppler ambiguity resolution

“…In order to avoid corruption by bad estimates from the areas with very weak backscatter or low contrast, quality criteria can be used to detect and remove bad estimates from the final Radon estimate, when the "spatial diversity" approach [10] is used. In addition to the data quality criteria of SNR and contrast used in [10], the following four …”