Synthetic Aperture Radar (SAR) interferometry (InSAR) uses phase differences between overlapping SAR images to estimate terrain height and terrain height changes. In addition, the coherence magnitude between the images is often used as a measure of the quality of the data and the processing. By modeling the SAR image data as independent circular Gaussian random variates, we develop the maximum likelihood (ML) estimates for interferogram phase, coherence magnitude, and the variance of the underlying circular Gaussian distribution. We show that the ML estimate of interferogram phase is equivalent to the standard technique of computing the phase of averaged complex returns. The ML estimate of the coherence magnitude depends on the estimated interferogram phase. In comparison, the sample coherence magnitude estimate based on amplitudes alone is badly biased. We also derive the Cramer-Rao bound for each ML estimate. The ML estimate of interferogram phase is close to this bound for moderate to high coherence values. Similarly, the coherence magnitude is close to the bound for values of coherence greater than approximately 1/2. For coherence magnitudes less than 1/2, the ML estimate of coherence magnitude is biased for data samples sizes up to 16 samples.

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Abstract-An onboard synthetic aperture radar (SAR) signaldata reduction algorithm called Flexible Block Adaptive Quantization (FBAQ) was developed in 1994 for the Advanced SAR (ASAR) on the ENVISAT satellite. This paper presents work done in a follow-on study that examined the impact of the datareduction algorithm on the accuracy of two digital elevation models (DEM) produced by using the technique of repeat-pass SAR interferometry. All three allowable data compression ratios were investigated to determine the maximum compression ratio appropriate for SAR interferometry. Based on the scenes studied in this paper, it was concluded that a reduction from 8 to 4-bits/sample was the maximum data reduction ratio appropriate for precision SAR interferometry, while 8 to 3-bits/sample and 8 to 2-bits/sample encoding were only appropriate for lessdemanding wide-swath applications.Index Terms-Block adaptive quantization, data compression, satellite interferometry.

Satellite SAR interferometry estimates terrain elevation by comparing two complex SAR images collected from separate passes over common ground. We have implemented prototype software to perform satellite interferometry. Two raw SEASAT data sets from Northern Canada were used to test the software. This data presents an example of SAR interferometry in a northern area and also demonstrates the sensitivity of interferogram phase to the conditions of the underlying data. Complications to our analysis were introduced by the plethora of frozen and unfrozen waterbodies in the area along with varying weather conditions during the three day interval between data takes. Finally, irregularities in the data produced aninconsistent interferogram that could not be meaningfully transformed to height estimates. However, the generation of fringes from the Arctic imagery demonstrates that satellite interferometry is possible in near polar locations.

Abstract. We present and analyze an algorithm for the production of accurate digital elevation models (DEMs) using interferometric synthetic aperture radar (InSAR). The algorithm requires minimal manual intervention, as a result of using coarse or low-quality DEMs in the InSAR processing stream. The low-quality DEM data are used to estimate the relative geometry (baseline parameters) of the SAR systems. The baseline parameters are estimated during two different stages of InSAR processing: (i) during interferogram conditioning, where the raw interferogram is preconditioned in preparation for phase unwrapping; and (ii) during production of the final InSAR-updated DEM. Analysis of the algorithm shows that the estimated baseline parameters result in an output InSAR DEM with approximately the same mean and trends in range and azimuth as the input DEM. This is achieved because the new algorithm allows the baseline parameters to absorb errors due to offsets and trends in the auxiliary parameters, such as range distance and satellite altitudes, and in the unwrapped phase. We have demonstrated the ability of the algorithm to improve DEMs of various qualities using RADARSAT-1 InSAR data. The generated DEMs have standard deviations of 12-20 m with respect to a control DEM with an accuracy of 3 m standard deviation. This represents a two to four times improvement in height accuracy compared with the input DEMs.Résumé. Nous présentons et analysons un algorithme pour la production de modèles numériques de terrain (MNT) de précision utilisant des données interférométriques radar à synthèse d'ouverture (InSAR). L'algorithme requiert une intervention manuelle minimale dû au fait qu'on utilise des MNT de qualité grossière ou faible dans la procédure de traitement InSAR. Les données MNT de qualité réduite sont utilisées pour estimer la géométrie relative (paramètres de référence) des systèmes ROS. Les paramètres de référence sont estimés au cours de deux étapes différentes du traitement InSAR : (i) durant le conditionnement de l'interférogramme où l'interférogramme brut est pré-traité en préparation pour le développement de phase, et (ii) durant la production du MNT final mis à jour par InSAR. L'analyse de l'algorithme montre que les résultats des paramètres de référence estimés constituent un MNT InSAR de sortie ayant approximativement la même moyenne et tendances en portée et azimut que le MNT d'entrée. Ceci est possible grâce au nouvel algorithme qui permet aux paramètres de référence d'absorber les erreurs dues aux décalages et tendances dans les paramètres auxiliaires, comme la distance en portée et les altitudes du satellite, et dans la phase non développée. Nous avons démontré le potentiel de l'algorithme dans l'amélioration des MNT de qualités diverses en utilisant des données InSAR de RADARSAT-1. Les MNT générés donnent des erreurs standard de 12-20 m par rapport à un MNT de contrôle, avec une précision de 3 m au niveau de l'erreur standard. Ceci représente une amélioration de l'ordre de deux à quatre fois supérieure en p...