Rodrigo Affonso de Albuquerque Nóbrega

An object-based approach to detect road features for informal settlements near Sao Paulo, Brazil

Continuous mapping efforts have been required to monitor intense urbanization processes of large cities of developing countries. The uncontrolled sprawl occurring in the vicinity of Sao Paulo, Brazil since the 70’s illustrates this scenario. Considering that urban sprawl causes changes to road networks, monitoring new roads as well as the changes along existing roads can provide significant information for urban management. Due to the lack of coverage in historical and accurate aerial image and map products, existing or even outdated image data are unavailable for planning urban land use with substantial relevance. The recent availability of high resolution satellite images, beginning with the IKONOS II in 1999, has enabled urban applications of Remote Sensing. Unfortunately, traditional techniques employed to detect land cover information based on per-pixel analysis have yielded unsatisfactory results in urban application of high resolution satellite images. In this sense, enhanced capabilities and successful applications of object-based classification have stimulated research to develop new methodologies to provide geoinformation. To this end, road extraction research has been formulated to segment object-primitives from images and to use the resultant information to devise enhancements to improve the road detection and classification process. This chapter reports the use of object-based image classification applied to road detection in informal settlements areas. An 11-bit IKONOS image was employed as the primary remote sensing data for classification. Principal components and semi-homogeneous segmented area products (segmentation products) were computed and used to define custom features. Auxiliary data were calculated from spectral information in combination with geometric information extracted from segments. Contextual information was also employed to support the implementation of a classification rule base. The classification rule base eliminated vegetated areas and then considered impervious surface and bare soil areas, as well as the width, length, asymmetry and the neighborhood relationship for the objects to detect road features. Comparisons between the automatic approach results and manually extracted road feature areas delivered insight regarding omission and commission error by area counting as well as metrics employed to determine completeness and correctness of extracted road features by linear correspondence analysis attest to the efficiency of the methodology. Results indicate that the methodology produces significant information and offers improvements over traditional pixel-based methods of road extraction and classification.

Detection of slump slides on earthen levees using polarimetric SAR imagery

Key results are presented of an extensive project studying the use of synthetic aperture radar (SAR) as an aid to the levee screening process. SAR sensors used are: (1) The NASA UAVSAR (Uninhabited Aerial Vehicle SAR), a fully polarimetric L-band SAR capable of sub-meter ground sample distance; and (2) The German TerraSAR-X radar satellite, also multi-polarized and featuring 1-meter GSD, but using an X-band carrier. The study area is a stretch of 230 km of levees along the lower Mississippi River. The L-band measurements can penetrate vegetation and soil somewhat, thus carrying some information on soil texture and moisture which are relevant features to identifying levee vulnerability to slump slides. While X-band does not penetrate as much, its ready availability via satellite makes multitemporal algorithms practical. Various feature types and classification algorithms were applied to the polarimetry data in the project; this paper reports the results of using the Support Vector Machine (SVM) and back-propagation Artificial Neural Network (ANN) classifiers with a combination of the polarimetric backscatter magnitudes and texture features based on the wavelet transform. Ground reference data used to assess classifier performance is based on soil moisture measurements, soil sample tests, and on site visual inspections.

Use of remote sensing to screen earthen levees

Multi-polarized L-band Synthetic Aperture Radar is investigated for its potential to screen earthen levees for weak points. Various feature detection and classification algorithms are tested for this application, including both radiometric and textural methods such as grey-level co-occurrence matrix and wavelet features.

Bridging decision‐making process and environmental needs in corridor planning

Purpose – The aim of this paper is to clarify the spatial multi‐criteria workflow for stakeholders and decision makers, for which feedback rankings are vital to the success of the transportation planning.Design/methodology/approach – The experimental approach was designed to integrate in a novel fashion both analytical hierarchy process (AHP) and multi‐criteria decision making (MCDM) within a geospatial information system (GIS) framework to deliver visual and objective tabular results useful to estimate environmental costs of the alignments generated. The method enables ranking, prioritization, selection, and refinement of preferred alternatives. The Interstate‐269, the newly planned bypass of Memphis‐TN, for which a recent environmental impact study (EIS) was completed, was selected as the experiment test‐bed.Findings – The results indicate that the approach can automate the delivery of feasible alignments that closely approximate those generated by traditional approaches. Furthermore, via integration of...

Fusion of synthetic aperture radar and hyperspectral imagery to detect impacts of oil spill in Gulf of Mexico

The Deepwater Horizon blowout in the Gulf of Mexico resulted in one of the largest accidental oil disasters in U.S. history. NASA acquired radar and hyperspectral imagery and made them available to the scientific community for analyzing impacts of the oil spill. In this study, we use the L-band quad-polarized radar data acquired by Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and Hyperspectral Imagery (HSI) from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) optical sensor. The main objective of this research is to apply fusion techniques on polarimetric radar and hyperspectral imagery to investigate the benefit of fusion for improved classification of coastal vegetation contaminated by oil. In this approach, fusion is implemented at the pixel level by concatenating the hyperspectral data with the high resolution SAR data and analyze the fused data with Support Vector Machine (SVM) classification algorithm.

Earthen levee monitoring with Synthetic Aperture Radar

The latest results are presented from an ongoing study of the use of multi-polarized Synthetic Aperture Radar as an aid in screening earthen levees for weak points. Both L-band airborne and X-band spaceborne radars are studied, using the NASA UAVSAR and the German TerraSAR-X platforms. Feature detection and classification algorithms tested for this application include both radiometric and textural methods. Radiometric features include both the simple backscatter magnitudes of the HH, VV, and HV channels as well as decompositions such as Entropy, Anisotropy, and Alpha angle. Textural methods include grey-level co-occurrence matrix and wavelet features. Classifiers tested include Maximum Likelihood and Artificial Neural Networks. The study area includes 240 km of levees along the lower Mississippi River. Results to date are encouraging but still very preliminary and in need of further validation and testing.

A Machine Learning Framework for Detecting Landslides on Earthen Levees Using Spaceborne SAR Imagery

Earthen levees have a significant role in protecting large areas of inhabited and cultivated land in the United States from flooding. Failure of the levees can result in loss of life and property. Slough slides are among the problems which can lead to complete levee failure during a high water event. In this paper, we develop a method to detect such slides using X-band synthetic aperture radar (SAR) data. Our proposed methodology includes: radiometric normalization of the TerraSAR image using high-resolution digital elevation map (DEM) data; extraction of features including backscatter and texture features from the levee; a feature selection method based on minimum redundancy maximum relevance (mRMR); and training a support vector machine (SVM) classifier and testing on the area of interest. To validate the proposed methodology, ground-truth data are collected from slides and healthy areas of the levee. The study area is part of the levee system along the lower Mississippi River in the United States. The output classes are healthy and slide areas of the levee. The results show the average classification accuracies of approximately 0.92 and Cohens kappa measures of 0.85 for both healthy and slide pixels using ten optimal features selected by mRMR with a sigmoid SVM. A comparison of the SVM performance to the maximum likelihood (ML) and back propagation neural network (BPNN) shows that the average accuracy of the SVM is superior to that of the BPNN and ML classifiers.

Regional and local topography subdivision and landform mapping using SRTM-derived data: a case study in southeastern Brazil

Topographic subdivisions and landforms are important relief characteristics that serve as a basis for several types of regional and local planning. This paper presents an assessment of the application of a global landform classification method on a regional scale using Sao Paulo State as the study area and on a local scale using the city of Sao Paulo as the study area. In addition, a new approach that includes elevation derivatives for local analysis is presented. The study hypothesizes that the automated object-based classification of topography from shuttle radar topography mission (SRTM) data method could also be used for local mapping when supported by elevation derivative data. SRTM data were used in the regional approach and post-processed elevation data derived from SRTM were used for the local analysis. The results were compared with the best available geomorphological maps and topographic surface descriptions of the region. The new method resulted in a regional-scale product in which the boundaries and features of the topography matched those in the geomorphological maps and in the literature. The method did not perform well when classifying the local topographic subdivisions of Sao Paulo, even when using the interpolated elevation data. However, the surface edges and shapes identified in the topographic maps were represented in the resulting map. To refine the results, a new approach was proposed using data derived from a digital elevation model, such as drainage densities, horizontal and vertical curvatures, and slope gradients. The use of these products in the image segmentation process and classification criteria was fundamental for obtaining the results. Theoretical thresholds were used to define the relief classes, and landform characteristics were taken into account in developing the landform map. The success of this new approach is attributed to the comprehensive database that supported the topographic subdivision analysis. In summary, this study indicates that a method developed for use on a global scale can be replicated for use on a regional scale but not on a local scale. The new approach produced reasonable results and can be used in other regions. Greater detail can be obtained using various thresholds of horizontal and vertical curvatures, for example, when delineating hazard areas. The products have potential applications in urban planning, ecological-economic zoning, urban drainage, hazard mitigation, environmental issues, erosional dynamics and transportation planning.

Evaluation of Multiple Classifier Systems for Landslide Identification in LANDSAT Thematic Mapper (TM) Images

Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for landslide risk management, remote sensing usage does have a few drawbacks. The aforementioned events commonly occur at high steep slope regions, frequently associated with shadow occurrence in satellite images, which impairs the identification process and results in low accuracy classifications. In this sense, this paper aims to evaluate the accuracy of different ensembles of multiple classifier systems (MCSs) for landslide scar identification. A severe landslide event on a steep slope with a high rainfall rate area in the southeast region of Brazil was chosen. Ten supervised classifiers were used to identify this severe event and other possible features for the LANDSAT thematic mapper (TM) from June of 2000. The results were evaluated, and nine MCSs were constructed based on the accuracy of the classifiers. Voting was applied through the ensemble method, coupled with contextual analysis and random selection tie-breaker methods. Accuracy was evaluated for each classification ensemble, and a progressive enhancement in the ensemble accuracy was noted as the least accurate classifiers were removed. The best accuracy for landslide identification emerged from the ensemble of the three most accurate classification results. In summary, MCS application generally improved the classification quality and led to fewer omission errors, coupled with a better classification percentage for the ‘landslide’ class. However, the MCS ensemble algorithm selection must be customized to the purpose of the classification. It is crucial to assess single accuracy indicators of each algorithm to ascertain those with the most consistent performance regarding the final results.

Multi-Scale GIS Data-Driven Method for Early Assessment of Wetlands Impacted by Transportation Corridors

© 2012 Nobrega et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Multi-Scale GIS Data-Driven Method for Early Assessment of Wetlands Impacted by Transportation Corridors