Leila Maria Garcia Fonseca

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—Image segmentation covers techniques for splitting one image into its components as homogeneous regions. This letter presents a resegmentation approach applied to urban images. Resegmentation represents the set of adjustments from a previous segmentation in which the elements are small regions with a high degree of spectral similarity (a condition known as(More)
This article presents a new approach for image segmentation applied to urban imagery. The proposed method is called re-segmentation because it uses a previous over-segmented image as input to generate a new set of objects more adequate to the application of interest. For urban objects such as roofs, building and roads, the algorithm tries to generate(More)
This paper presents several extensions of the basic CPA algorithm. First we compare CPA to standard corner detection algorithms and then turn to the question of selecting control points with adequate dispersion since this is crucial for accurate registration. Two selection methods are proposed. The first consists of clustering the control points via the(More)
Although a huge amount of remote sensing data has been provided by Earth observation satellites, few data manipulation techniques and information extraction in large data sets have been developed. In this context, the present paper aims to show a new system for spatial data mining, and two test cases applied to land use change in the Brazilian Amazon(More)
Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in one year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to(More)
In statistical pattern recognition, mixture models allow a formal approach to unsupervised learning. This work aims to present a modification of the Expectation-Maximization clustering method applied to remote sensing images. The stability of its convergence has been increased by supplying the results of the well-known K-Means algorithm, as seed points.(More)
Image segmentation is one of the most challenging steps in image processing. Its results are used by many other tasks regarding information extraction from images. In remote sensing, segmentation generates regions according to found targets in a satellite image, like roofs, streets, trees, vegetation, agricultural crops, or deforested areas. Such regions(More)