Mihai Datcu

Maximum entropy methods for despeckling and resampling synthetic aperture radar images of rough terrain

SAR systems, like any coherent imaging system, are subject to (I) speckling effects, which considerably reduce the useful detail within the acquired scenes and (II), strong geometric distortions. Furthermore, the resolution of SAR systems is comparable to the size of many of the objects of interest in the scene. Our paper proposes a unified treatment of these problems within the framework of probabilistic inference. Despeckling and segmentation are the main objectives only in the first case. In the second case, due to the strong geometric aberrations introduced by the SAR image formation system, the emphasis is on image resampling, with speckle reduction and image segmentation as collateral, but strongly related issues. In both cases, the model is built upon the statistical properties of the speckle noise and the SAR image formation equations.

Wavelets: a universal tool for the processing of remote sensing data?

During the last years, wavelets have become very popular in the fields of signal processing and pattern recognition and have led to a large number of publications. In the discipline of remote sensing several applications of wavelets have emerged, too. Among them are such diverse topics as image data compression, image enhancement, feature extraction, and detailed data analysis. On the other hand, the processing of remote sensing image data - both for optical and radar data - follows a well-known systematic sequence of correction and data management steps supplemented by dedicated image enhancement and data analysis activities. In the following we will demonstrate where wavelets and wavelet transformed data can be used advantageously within the standard processing chain usually applied to remote sensing image data. Summarizing potential wavelet applications for remote sensing image data, we conclude that wavelets offer a variety of new perspectives especially for image coding, analysis, classification, archiving, and enhancement. However, applications requiring geometrical corrections and separate dedicated representation bases will probably remain a stronghold of classical image domain processing techniques.

Wavelet feature coding for quicklook synthetic aperture radar images: an image epitome

Applications like real time on-board processed SAR image transmission for ships, ice or oil slick monitoring and detection, and also ground segment applications require a new philosophy for data representation and compression. This also includes high speed and high resolution data dissemination as for example monitoring of floodings, where the transmission in near real time of high resolution data via Internet could be a major improvement on mission level. Conventional SAR quicklook images do not satisfy the spatial resolution requirements for such applications. As an alternative, we propose a new visual epitome based on a wavelet feature coding technique for SAR images in order to preserve the spatial resolution and to achieve high compression factors. Combining data compression, despeckling, and image restoration allows us to reach compression rates of up to about 850, thus permitting easy storage in centralized archives as well as rapid dissemination over standard networks. After decompression at the user site, the quality of the quicklook images permit the visual inspection and analysis of all spatially important image details. This becomes apparent when comparing conventional multilook quicklook images with wavelet feature coded decompressed counterparts. Typical examples will be demonstrated. Due to the extremely high compression rates, the radiometric quality of the quicklook images is degraded. However, the use of wavelet multiresolution representation of the images bears the additional potential of progressive transmission that is stopped interactively when an acceptable level of radiometric fidelity is reached. The decompression effort is small, robust algorithms are available and further compression optimizations are being investigated.

Linear versus non-linear analysis of relevant scatterers in high resolution SAR images

With the increase of synthetic aperture radar (SAR) sensor resolution, SAR images could include a large variety of interesting real man-made structures. Therefore, a more detailed analysis and a finer description of SAR images of urban areas are needed for a better understanding of the scene. Nevertheless, recognizing scenes using high resolution SAR images requires the capability to identify relevant signal signatures (called also descriptors), depending on variable image acquisition geometry, arbitrary objects poses and configurations. Among feature extraction methods, we propose to use principal components analysis (PCA) and/or independent components analysis (ICA), in order to exploit deeper the nature of SAR signatures. In this paper, both a description of our work and a presentation of our preliminary classification performance results will be provided.

Phase information contained in meter-scale SAR images

The properties of single look complex SAR satellite images have already been analyzed by many investigators. A common belief is that, apart from inverse SAR methods or polarimetric applications, no information can be gained from the phase of each pixel. This belief is based on the assumption that we obtain uniformly distributed random phases when a sufficient number of small-scale scatterers are mixed in each image pixel. However, the random phase assumption does no longer hold for typical high resolution urban remote sensing scenes, when a limited number of prominent human-made scatterers with near-regular shape and sub-meter size lead to correlated phase patterns. If the pixel size shrinks to a critical threshold of about 1 meter, the reflectance of built-up urban scenes becomes dominated by typical metal reflectors, corner-like structures, and multiple scattering. The resulting phases are hard to model, but one can try to classify a scene based on the phase characteristics of neighboring image pixels. We provide a cooking recipe of how to analyze existing phase patterns that extend over neighboring pixels.

Chirp correlation in the wavelet domain

The wavelet transform developed during the last years into a mature and very pragmatic formalism for the analysis of the scale behavior of signals. However, it also remains a tool to serve its very initial goal: the time-frequency analysis. In this article we summarize the basics of time-frequency- scale formalism for signal representation and analysis, and we overview several applications with promising results for the SAR signal processing.