Gilson Alexandre Ostwald Pedro da Costa

Hidden Markov Models for crop recognition in remote sensing image sequences

This work proposes a Hidden Markov Model (HMM) based technique to classify agricultural crops. The method uses HMM to relate the varying spectral response along the crop cycle with plant phenology, for different crop classes, and recognizes different agricultural crops by analyzing their spectral profiles over a sequence of images. The method assigns each image segment to the crop class whose corresponding HMM delivers the highest probability of emitting the observed sequence of spectral values. Experimental analysis was conducted upon a set of 12 co-registered and radiometrically corrected LANDSAT images of region in southeast Brazil, of approximately 124.100ha, acquired between 2002 and 2004. Reference data was provided by visual classification, validated through extensive field work. The HMM-based method achieved 93% average class accuracy in the identification of the correct crop, being, respectively, 10% and 26% superior to multi-date and single-date alternative approaches applied to the same data set.

Assessment of Binary Coding Techniques for Texture Characterization in Remote Sensing Imagery

This letter investigates the use of rotation invariant descriptors based on Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) for texture characterization in the context of land-cover and land-use classification of Remote Sensing (RS) optical image data. Very high resolution images from the IKONOS-2 and Quickbird-2 orbital sensor systems covering different urban study areas were subjected to classification through an object-based approach. The experiments showed that the discrimination capacity of LBP and LPQ descriptors substantially increased when combined with contrast information. This work also proposes a novel texture descriptors assembled through the concatenation of the histograms of either LBP or LPQ descriptors and of the local variance estimates. Experimental analysis demonstrated that the proposed descriptors, though more compact, preserved the discrimination capacity of bi-dimensional histograms representing the joint distribution of textural descriptors and contrast information. Finally, the paper compares the discrimination capacity of the LBP- and LPQ-based textural descriptors with that of features derived from the Gray Level Co-occurrence Matrices (GLCM). The related experiments revealed a noteworthy superiority of LBP and LPQ descriptors over the GLCM features in the context of RS image data classification.

Genetic adaptation of segmentation parameters

This work presents a method for the automatic adaptation of segmentation parameters based on Genetic Algorithms. An intuitive and computationally simple fitness function, which expresses the similarity between the segmentation result and a reference provided by the user, is proposed. The method searches the solution space for a set of parameter values that minimizes the fitness function. A prototype including an implementation of a widely used segmentation algorithm was developed to assess the performance of the method. A set of experiments with medium and high spatial resolution remote sensing image data was carried out and the method was able to come close to the ideal solutions.

Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features

Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an “urban area” is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, “urban area” extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO) data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR) data prove the usefulness and flexibility of the framework.

A knowledge-based, transferable approach for block-based urban land-use classification

In this work we propose a knowledge-based approach for land-use classification of city blocks through the automatic interpretation of very-high-resolution remote-sensing imagery. Our approach is founded on geographic object-based image analysis (GEOBIA) concepts and is concerned with transferability across distinct knowledge representation formalisms. This paper therefore investigates the viability of translating a high-level description of the interpretation problem into the particular knowledge representation structures and interpretation strategies of two different software platforms, namely the proprietary Definiens Developer system and the open-source InterIMAGE system. Initially, textual descriptions of the land-use classes of interest were created by photo interpreters. Then, generic class descriptions were defined as a system-independent knowledge model, which was subsequently translated into interpretation projects in the different systems. Altogether 49 blocks located on two different test-sites in the city of São Paulo (Brazil) were considered in the experiments. Although the classification results from the Definiens Developer system were slightly better than those obtained with the InterIMAGE system, we concluded that both systems have been shown to be equally qualified to implement the target application properly through adaptation of the generic knowledge model.

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.

An object-based image analysis approach based on independent segmentations

Geographic Object-Based Image Analysis (GEOBIA) makes it possible to exploit a number of new features in the remote sensing image classification process. Such possibility is brought by the introduction of a segmentation step in the analysis process. The new features refer to aggregated spectral pixel values, textural, morphological and topological features computed for the different image segments. The usual segmentation approach in GEOBIA works relies on a hierarchy of segmentations, each level related to a number of object classes that have similar sizes, i.e., are detectable in a similar scale. We, therefore, propose an approach founded on the assumption that if segmentations are not specialized for each object class, then many of the new segment features cannot be properly exploited in the classification process. The proposed approach relies on a specific rule to solve eventual spatial conflicts among different segmentations. Preliminary experimental results show that the proposed approach performed better that the usual one.

Towards distributed region growing image segmentation based on MapReduce

Image segmentation is a critical step in image analysis, and usually involves a high computational cost, especially when dealing with large volumes of data. Given the significant increase in the spatial, spectral and temporal resolutions of remote sensing imagery in the last years, current sequential and parallel solutions fail to deliver the expected performance and scalability. This work proposes a scalable and efficient segmentation method, capable of handling efficiently very large high resolution images. The proposed solution is based on the MapReduce model, which offers a highly scalable and reliable framework for storing and processing massive data in cloud computing environments. The solution was implemented and validated using the Hadoop platform. Experimental results attest the viability of performing region growing segmentation in the MapReduce framework.

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.