Dário Augusto Borges Oliveira

A New Cloud Computing Architecture for the Classification of Remote Sensing Data

This paper proposes a new distributed architecture for supervised classification of large volumes of earth observation data on a cloud computing environment. The architecture supports distributed execution, network communication, and fault tolerance in a transparent way to the user. The architecture is composed of three abstraction layers, which support the definition and implementation of applications by researchers from different scientific investigation fields. The implementation of architecture is also discussed. A software prototype (available online), which runs machine learning routines implemented on the cloud using the Waikato Environment for Knowledge Analysis (WEKA), a popular free software licensed under the GNU General Public License, is used for validation. Performance issues are addressed through an experimental analysis in which two supervised classifiers available in WEKA were used: random forest and support vector machines. This paper further describes how to include other classification methods in the available software prototype

An open source object-based framework to extract landform classes

This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms.

A Set of Methods to Support Object-Based Distributed Analysis of Large Volumes of Earth Observation Data

The rapid increase in the number of aerial and orbital Earth observation systems is generating a huge amount of remote sensing data that need to be readily transformed into useful information for policy and decision makers. This exposes an urgent demand for image interpretation tools that can deal efficiently with very large volumes of data. In this work, we introduce a set of methods that support distributed processing of georeferenced raster and vector data in a computer cluster, which may be a virtual cluster provided by cloud computing infrastructure services. The set of methods comprise a particular technique for indexing distributed georeferenced datasets, as well as strategies for distributing efficiently the processing of spatial context-aware operations. They provide the means for the development of scalable applications, capable of processing large volumes of geospatial data. We evaluated the proposed methods in a remote sensing image interpretation application, built on the MapReduce framework, and executed in a cloud computing infrastructure. The experimental results corroborate the capacity of the methods to support efficient handling of very large earth observation datasets.

On the architecture of a big data classification tool based on a map reduce approach for hyperspectral image analysis

Advances in remote sensors are providing exceptional quantities of large-scale data with increasing spatial, spectral and temporal resolutions, raising new challenges in its analysis, e.g. those presents in classification processes. This work presents the architecture of the InterIMAGE Cloud Platform (ICP): Data Mining Package; a tool able to perform supervised classification procedures on huge amounts of data, on a distributed infrastructure. The architecture is implemented on top of the MapReduce framework. The tool has four classification algorithms implemented taken from WEKAs machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines. The SVM classifier was applied on datasets of different sizes (2 GB, 4 GB and 10 GB) for different cluster configurations (5, 10, 20, 50 nodes). The results show the tool as a potential approach to parallelize classification processes on big data.

Weighting Estimation for Texture-Based Face Recognition Using the Fisher Discriminant

Texture-based automatic face recognition (AFR) methods find global similarities between two faces by computing their local regional similarities. A novel method based on Fisher discriminant analysis is proposed to estimate each regions contribution to the global similarity score. Experimental results show that the method considerably improves recognition performance for texture-based AFR.

The introduction of navigation in liver surgery in Brazil

The authors thoroughly report the development, the technical aspects and the performance of the first navigated liver resections, by laparotomy and laparoscopy, in Brazil, done at the National Cancer Institute, Ministry of Health, using a surgical navigator.

Cognitive approaches and optical multispectral data for semi-automated classification of landforms in a rugged mountainous area

This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework owns a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of the semantic network, which constitutes a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE is presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic and drove the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms. * Corresponding author.

Using the cognitive platform interimage to identify geomorphological features

This paper introduces a new open source, knowledge-based platform for automatic image interpretation called InterIMAGE. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE is presented. The paper also reports an experiment based on a Geomorphology application. The experiments objective was the automatic identification of geomorphological features using a knowledge model that considered a set of textural and geomorphometric variables extracted from a digital elevation model obtained from a pair of stereoscopic ASTER/Terra images. The experiments results showed a strong agreement between the automatically classified scene and a reference map. A similar experiment was carried out with a commercial, image interpretation software - Definiens Professional -, and the results attained with both software packages were comparable.