We address the problem of airborne high resolution two-dimensional inverse synthetic aperture radar (ISAR) side-view imaging of ship targets. Using a simple model of the ship motions avoids the use of advanced joint-time frequency (JTF) transforms. A robust processing scheme including motion estimation and correction, optimal processing time and duration estimation and target shape extraction are developed. We stress the fact that the robustness of this processing leads to a single ISAR image analysis and that no merging of data from a set of radar images is necessary.
CORRESPONDENCE
Automatic extraction of buildings in urban scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly with the emergence of LiDAR systems since mid-1990s. However, in reality, this task is still very challenging due to the complexity of building size and shape, as well as its surrounding environment. Active contour model, colloquially called snake model, which has been extensively used in many applications in computer vision and image processing, has also been applied to extract buildings from aerial/satellite imagery. Motivated by the limitations of existing snake models dedicated to the building extraction, this paper presents an unsupervised and automatic snake model to extract buildings using optical imagery and an unregistered airborne LiDAR dataset, without manual initial points or training data. The proposed method is shown to be capable of extracting buildings with varying color from complex environments, and yielding high overall accuracy.
SAR images can be used to help ship routing in sea-ice conditions. In this study, we focus on the Antarctic region where no multi-year ice nor big ice floes are to be found. As a matter of fact, each clutter obeys to a backscattering mechanism that induces a specific pixel distribution and our attempt is to identify automatically the correct distribution for each ice type. The problem is that of generalized mixture estimation and unsupervised image classification. In this work, we modelled the mixture with distributions from the Pearson's system. Parameters estimation is realized according to the ICE algorithm in the context of hidden Markov chains. The results obtained from the Pearson's system are compared to the ones obtained with a classical mixture of Gaussian distributions.
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