In this work we compare two different approaches to the use of multiple scales in the classification process of satellite SAR images. These are (I) the multi-scale co-occurrence texture analysis and (II) the semivariogram approach. Moreover, we propose a scheme for optimizing the co-occurrence window size and the semivariogram lag distances in terms of classification accuracy performance. To improve the results even further, we introduce a methodology to compute the co-occurrence features with a window consistent with the local scale, provided by the semivariogram analysis.Examples of satellite SAR image segmentation for urban area characterization are shown to validate the procedure.
"This is why every day more the leaning is to process images with a multi-scale or hierarchical technique , , that offer as result a segmentation of the image for each scale or level, respectively. Multi-scale techniques are mainly based on study the evolution of the contours through different scales, by blurring the image more in each scale and then performing a new segmentation. "
[Show abstract][Hide abstract] ABSTRACT: In image segmentation it is well known that a given image can be analyzed with different detail levels, this is why some hierarchical approaches have been proposed to give a different segmentation for each detail level. Most of these proposals are specially designed for precise and well defined regions. However regions usually have blurred contours, soft color shades, and brightness that give rise to the problem of the imprecision in the regions. In this paper we face both problems considering the imprecision of the regions at the definition of the criteria to obtain a hierarchy detail levels. Concretely, we propose to calculate a similarity relation between fuzzy regions, based on two measures that take into account the imprecision in the transition between the regions, as well as the likeness of their characteristics. Then we use this fuzzy similarity relation to obtain a nested hierarchy of fuzzy segmentations by means of its alpha-cuts. In this way we obtain a tool to easily change the detail level and obtain a new fuzzy segmentation of the image, just changing the value of alpha.
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on; 07/2008
"Damage maps will be computed for the whole data set, but they will be significant in urban areas only. This requires a urban/surrounding discrimination by means of a preliminary step, which may be obtained by the same SAR image, using for instance the textural feature analysis proposed in . A more efficient way would be however to use ancillary data. "
[Show abstract][Hide abstract] ABSTRACT: In this paper, the problem of rapid earthquake damage detection in urban areas using multitemporal synthetic aperture radar data is addressed. It is shown that the combination of intensity and phase features enhances the damage pattern extracted from the data temporal stack using a spatially aware classifier. Moreover, the use of ancillary data, easily available for urban areas, further improves the accuracy by discarding uninteresting parts of the scene and forcing homogeneous classification within city blocks to avoid "class-blurring" effects consequential to the window-based computation of relevant measures. The procedure is validated based on results for the town of Bam, Iran, and compared with ground-based survey maps
IEEE Transactions on Geoscience and Remote Sensing 07/2007; 45(6-45):1582 - 1589. DOI:10.1109/TGRS.2006.885392 · 3.51 Impact Factor
"To make the whole procedure as much data-driven as possible, automatic methodologies to extract the scale of an image are required. This is one interesting line of research , actively pursued but very difficult to work at very high spatial resolution given the enormous number of possible combinations and the abrupt scale changes that may occur moving from one part to another even of the same town. One of the major points related to this research is therefore an efficient and safe feature selection strategy, able to maintain the selected set into a reasonable dimensionality, but also to adapt to different problems. "
[Show abstract][Hide abstract] ABSTRACT: This paper explains some of the the goals and objectives of the newly started HYPER-I-NET Marie Curie Research and Training Network. In particular, the requirements related to the definition and implementation of an efficient, adequate and sufficiently general data processing chain for hyperspectral data analysis are considered. Some of the research lines that are expected to play a central role in the activities of this network are also presented and briefly discussed.
IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2007, July 23-28, 2007, Barcelona, Spain, Proceedings; 01/2007
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