Teresa Barata

Automatic Recognition of Impact Craters on the Surface of Mars

This paper presents a methodology to automatically recognise impact craters on the surface of Mars. It consists of three main phases: in the first one the images are segmented through a PCA of statistical texture measures, fol- lowed by the enhancement of the selected contours; in a second phase craters are recognised through a template matching approach; in a third phase the rims of the plotted craters are locally fitted through the watershed transform.

Identification of martian polygonal patterns using the dynamics of watershed contours

This paper presents a methodology to automatically identify polygonal patterns on the surface of Mars. These structures, which are typical of periglacial regions, result from climate oscillations and present a wide variation in size, shape and topology and occur in different types of terrains with rather different constituents and spectral reflectances. The proposed approach is mainly based on the analysis of the dynamics of watershed contours and is successfully applied to a set of different types of patterned terrains of Mars shown by MGS/MOC images.

A morphological approach for feature space partitioning

A mathematical morphology-based methodology to construct decision region borders that geometrically model the training sets of points is presented in this letter. It is shown that the incorporation of the geometric features of the training sets leads to higher classification rates. Our approach is illustrated with two features of seven land-cover classes [forest (3), soil (2), vegetation, and water] constructed from remotely sensed images of a region in the center of Portugal.

Classification by mathematical morphology

A general methodology that explores the geometric characteristics presented by the training sets of each class (size, shape and orientation) in feature space, without making explicitly any statistical hypothesis, is presented in this paper. The respective decision region borders are constructed automatically and provide higher classification rates.

Improving classification rates by modelling the clusters of trainings sets in features space using mathematical morphology operators

The exploration of features presented by the training sets of each class (size, shape and orientation) in order to construct the respective decision region borders without making explicitly any statistical hypothesis is presented in this paper. Its incorporation allows one to define more correct decision borders since there is a significant improvement in the classification rates obtained. Mathematical morphology operators are preferentially used in this methodology, which is illustrated with two spectral features (wetness tasselled cap and NDVIs vegetation index) of seven land cover classes constructed from Landsat TM satellite images of central Portugal.

Multilingual Narrative Planetary Maps for Children

A set of children’s maps on the solid-surfaced planetary bodies of the solar system was developed in the framework of the program Europlanet 2012. The surfaces of the six bodies were illustrated by planetary scientists and graphic artists. This is the first project in which such detailed, hand-drawn lunar and planetary maps were created specifically for children, in the most common spoken languages of Europe. The map pages, prepared according to the latest data from space probes, are accompanied by a website where background information and interesting facts can be found in a form understandable for children. The topics covered were compiled with the help of questions that children asked about the maps. The map series was prepared with the support of the International Cartographic Association Commission on Planetary Cartography.

Morphological recognition of the spatial patterns of olive trees

A pair of algorithms to segment olive groves and recognize its individual trees in high spatial resolution remotely sensed images is presented. The developed algorithms are applied with success by exploiting the typical spatial patterns presented by this cover and are mainly based on mathematical morphology operators

Morphological Recognition of Olive Grove Patterns

This paper presents a methodology to segment olive groves in high spatial resolution remotely sensed images. The developed algorithms exploit the typical spatial patterns presented by this forest cover and are mainly based on mathematical morphology operators. It consists on identifying firstly the olive groves followed by the recognition of their individual trees. The methodology is tested with ortophotomaps from a region in central Portugal.

Petrographic Classification at the Macroscopic Scale Using a Mathematical Morphology Based Approach

A novel methodology for the automatic classification of the different textural classes that constitute a rock at the macroscopic scale is presented in this paper. The methodology starts with the segmentation of elementary textural units of the image followed by their classification, whose feature space partition results from the geometric modelling of the training sets. This approach uses mainly mathematical morphology operators and is tested with images of macroscopic polished surfaces of 14 types of portuguese grey granites.

Segmenting at higher scales to classify at lower scales. A mathematical morphology based methodology applied to forest cover remote sensing images

A methodology based on mathematical morphology to classify forest cover types in remote sensing images is presented. The information automatically extracted at higher scales (aerial photographs) by morphological segmentation approaches is afterwards used to classify different forest cover types at lower scales (satellite images). In this methodology the spectral process is guided by the spatial process, once the previous segmentation of the different textural elements is then used in the classification procedure, where the geometrical modelling of the shape of the training sets of points is also performed. Tests were done in a region of centre Portugal using aerial photographs and Landsat TM images for olive, cork oak, pine and eucalyptus trees.