Diferenças

Aqui você vê as diferenças entre duas revisões dessa página.

--- thales:ongoing_papers [2010/07/22 13:45] – tkorting
+++ thales:ongoing_papers [2010/07/22 18:15] (atual) – tkorting
@@ Linha 2: / Linha 2: @@
 ==== Writing ====
-  * **GeoDMA - A software for geographical pattern recognition.** Thales Sehn Korting, Leila Fonseca, Gilberto Camara. **EMS 2010?** //This work presents a novel software for pattern recognition applied to geographical and remote sensing data. GeoDMA stands for Geographical Data Mining Analyst, and is a free software designed to include data mining techniques to aid domain-experts in the detection of land cover and land use patterns in geographical databases. For this purpose, GeoDMA includes spatial and spectral features extraction, visual data analysis and supervised and unsupervised classification algorithms. GeoDMA has already been applied to detect intra-urban classes of land cover and to detect patterns of deforestation in the Brazilian Amazon.//
+  * **GeoDMA - A software for geographical pattern recognition.** Thales Sehn Korting, Leila Fonseca, Gilberto Camara. **EMS 2010?** //This work presents a novel software for pattern recognition applied to geographical and remote sensing data. GeoDMA stands for Geographical Data Mining Analyst, and is a free software designed to include data mining techniques to aid domain-experts in the detection of land cover and land use patterns in geographical databases. For this purpose, GeoDMA includes spatial and spectral features extraction, visual data analysis and supervised and unsupervised classification algorithms. GeoDMA has already been applied to detect intra-urban classes of land cover and to detect patterns of deforestation in the Brazilian Amazon.// **Este paper já está pronto, 2 páginas. Não valeria a pena enviá-lo?** {{:thales:ems_short.pdf}}
-  * **Interpreting Images with GeoDMA.** Thales Sehn Korting, Leila Fonseca, Gilberto Camara. **International Journal of Applied Earth Observation and Geoinformation, 2010?** //Object oriented analysis offers effective tools to represent the knowledge in a scene. Knowledge-based interpretation arises as an effective way to interpret remote sensing imagery. In this approach, human's expertise is organized in a knowledge base to be used as input of automated interpretation processes, thus enhancing performance and accuracy, and reducing at the same time the subjectivity in the interpretation process. Some systems such as Definiens, ENVI-FX, and more recently, InterIMAGE, have incorporated useful tools to aid the object-oriented classification. In this context, this work presents a system for object image analysis, called Geographical Data Mining Analyst (GeoDMA), which implements several mechanisms for automatic image interpretation. It performs image segmentation, objects feature extraction, supervised and unsupervised classification with raster, shape and cellular data sets. GeoDMA has been used for intra-urban land cover classification of high spatial resolution images and also to detect deforestation patterns in the Brazilian Amazon. Such different applications warrant the generalist behavior of the system. In this article, we discuss each module implemented in the system, the integration of objects obtained by segmentation to extract texture features and landscape metrics. We also show the main tools for data preprocessing, sample selection and feature visualization. Finally, one application of automatic urban interpretation is shown.//
+  * **Interpreting Images with GeoDMA.** Thales Sehn Korting, Leila Fonseca, Gilberto Camara. [[thales:geodma_paper|[Paper].]] **International Journal of Applied Earth Observation and Geoinformation, 2010?** //Object oriented analysis offers effective tools to represent the knowledge in a scene. Knowledge-based interpretation arises as an effective way to interpret remote sensing imagery. In this approach, human's expertise is organized in a knowledge base to be used as input of automated interpretation processes, thus enhancing performance and accuracy, and reducing at the same time the subjectivity in the interpretation process. Some systems such as Definiens, ENVI-FX, and more recently, InterIMAGE, have incorporated useful tools to aid the object-oriented classification. In this context, this work presents a system for object image analysis, called Geographical Data Mining Analyst (GeoDMA), which implements several mechanisms for automatic image interpretation. It performs image segmentation, objects feature extraction, supervised and unsupervised classification with raster, shape and cellular data sets. GeoDMA has been used for intra-urban land cover classification of high spatial resolution images and also to detect deforestation patterns in the Brazilian Amazon. Such different applications warrant the generalist behavior of the system. In this article, we discuss each module implemented in the system, the integration of objects obtained by segmentation to extract texture features and landscape metrics. We also show the main tools for data preprocessing, sample selection and feature visualization. Finally, one application of automatic urban interpretation is shown.//
 ==== Submitted ====
@@ Linha 10: / Linha 10: @@
   * **A re-segmentation approach to detect rectangular objects in high resolution imagery.** Thales Sehn Korting, Luciano Dutra, Leila Fonseca. **GRSL, 2010** //Image segmentation covers techniques for splitting one image into its components. This paper presents a re-segmentation approach applied to urban images. The interest elements, such as houses roofs, are considered to have a rectangular shape. Our technique finds and produces rectangular objects, setting to background the remaining elements to background. With an over-segmented image we connect bordering elements in a graph, known as {\it Region Adjacency Graph} -- RAG. By going into the graph, we search for the best cuts that may result in more rectangular objects, using a relaxation-like approach. Results show that the method was able to match rectangles, according to user-defined parameters, such as maximum levels of graph depth search and minimum degree of rectangularity for interest objects. //
-  * **GeoSOM applied to image segmentation.** Thales Sehn Korting, Leila Fonseca. **SITIS 2010** //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 differentiate land uses by classiﬁcation algorithms. In this paper we investigate a way to perform segmentation using a strategy to classify and merge spectrally and spatially similar pixels. For this purpose we use a geographical extension of the Self-Organizing Maps algorithm, which exploits the spatial correlation among near pixels. Trained neurons have similar properties to the main elements found in the image. By associating different labels to the image pixels, according the most similar neuron, neighboring pixels with the same label are merged into segments.//
+  * **A Geographical approach to Self-Organizing Maps algorithm applied to image segmentation.** Thales Sehn Korting, Leila Fonseca. **SITIS 2010** //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 differentiate land uses by classiﬁcation algorithms. In this paper we investigate a way to perform segmentation using a strategy to classify and merge spectrally and spatially similar pixels. For this purpose we use a geographical extension of the Self-Organizing Maps algorithm, which exploits the spatial correlation among near pixels. Trained neurons have similar properties to the main elements found in the image. By associating different labels to the image pixels, according the most similar neuron, neighboring pixels with the same label are merged into segments.//
   * **Assessment of a modified version of the EM algorithm for remote sensing data classification.** Thales Sehn Korting, Luciano Dutra, Guaraci Erthal, Leila Fonseca. **CIARP 2010** //This work aims to present an assessment of a modified version of the standard EM clustering algorithm for remote sensing data classification. The modification purpose was to improve the EM initialization by providing results of the well known K-means algorithm as seed points and to provide rules for decreasing the number of modes once a certain a priori cluster probability is very low. As observing clusters with very similar mean vectors but differing only on the covariance structures is not natural for remote sensing objects, another modification was proposed to avoid keeping clusters whose centres are too close. It was observed that this modified EM algorithm presented the best agreement with a reference map ploted on the scene when compared with standard K-means and SOM results.//