Costas Armenakis

A comparative analysis of image fusion methods

There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images. Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods. Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level. Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method. The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set. An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level.

A comparative analysis of scanned maps and imagery for mapping applications

Abstract In mapping organizations, the implementation of more automation coupled with the availability of heterogeneous data requires the investigation, adaptation and evaluation of new approaches and techniques. The demand for rapid mapping operations such as database generation and updating is continuously increasing. Due to the rising use of raster data, image analysis techniques have been investigated and tested in this study to introduce automation in the assessment of scanned topographic monochrome maps and Landsat 7 ETM+ imagery for feature separation and extraction in northern Canada. The work focuses on the detection and extraction of lakes—predominant features in the North—as well as on to their spatio-temporal comparison. Various approaches using digital image processing techniques were implemented and evaluated. Thresholding and texture measures were used to evaluate the potential of rapid extraction of certain topographic elements from scanned monochrome maps of northern Canada. A raster to vector approach (R→V) followed for the vectorization of these extracted features. The extraction of features from Landsat 7 ETM+ imagery involved image and theme enhancement by applying various image fusion and spectral transformations (e.g., Brovey, PCI-IMGFUSE, intensity–hue–saturation (IHS), principal component analysis (PCA), Tasseled Cap, Normalized Difference Vegetation Index (NDVI)), followed by image classification and thresholding. Tests showed that the approaches were more or less feature-dependent, while, at the same time, they can augment and significantly enhance the conventional topographic mapping methods. Following the analysis of the map and image data, change detection between two lake datasets was performed both interactively and in an automated mode based on the non-intersection of old and new features. The various approaches and methodology developed and implemented within a GIS environment along with examples, results and limitations are presented and discussed.

Prioritization of disaster risk in a community using GIS

Prioritization of disaster risk was carried out for a community in Toronto, Canada. Geographic information systems (GIS) were used for spatial analysis, including spatial overlays and clipping for extracting spatial and attribute information related to people’s vulnerability, critical infrastructure and landuse. In order to determine disaster risk, the overall community vulnerability was evaluated by combining social, economic, physical and environmental vulnerabilities. This paper uses the propane explosion incident as the case in point to demonstrate the methodology and procedure used to evaluate risk using GIS techniques. City of Toronto spatial data have been integrated with the study area to gather landuse information, identify risk zones based on the propane storage facility location and evaluate risks. Statistics Canada 2006 census data have been used for area demographics and people’s social and economic status. Vulnerability indicators were determined based on the GIS-derived spatial and attribute data for the hazard and evacuation zones followed by a quantitative spatial risk estimation and ranking. The methodology of this study, based on the risk evaluation and prioritization conducted, can be applied to future decision making in effective landuse planning and the development of risk management strategies.

Estimating spatial disaster risk in urban environments

Establishment of industries in urban zones increases the risk of technological disasters, thus affecting both population and the infrastructure. Disaster management includes organizational support building, risk assessment and prioritization, and analytical tools to support decision-making. A methodology has been proposed for estimating spatial disaster risk using the case of Toronto propane explosion of 2008, taking into account peoples vulnerability, critical infrastructure, and the spatial impact of the hazard. It integrates the use of GIS spatial analysis and disaster management principles and can be visualized in web-mapping browsers for planning purposes. This approach can be applied in developing strategies for future risk reduction, risk-based land use planning, resilience, and capacity-building.

Generation of three dimensional photo-realistic models from Lidar and image data

Light detection and ranging (Lidar) instruments collect high density and accurate three dimensional (3D) point clouds of scanned surfaces of objects. 3D building modelling from terrestrial Lidar requires the raw point cloud data to be processed. Through processing, noise and outliers are eliminated from the point cloud, and a 3D photo-realistic model is generated using image data. This effectively reduces redundant data and enhances the visual representation. This paper deals with point cloud processing and proposes methods to automate several of the processing procedures. Specifically, we implemented automatic 3D point cloud registration, automatic target recognition used for geo-referencing, automatic plane detection algorithm used for surface modelling, and texture mapping. The proposed approach leads to the generation of accurately geo-referenced three dimensional (3D) photo-realistic models from point clouds and digital imagery.

Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing

A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH) method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1) feature extraction; (2) similarity measure; and matching, and (3) estimating exterior orientation parameters (EOPs) of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process.

Spatial database updating using active contours for multispectral images: application with Landsat 7

This paper presents a fully automated approach for area detection and delineation based on multispectral images and features from a topographic database. The vectors residing in the database are refined using active contours (snakes) according to updated information provided by the multispectral images. The conventional methods of defining the external energy guiding the deformation of the snake based on: (1) statistical measures; or (2) gradient-based boundary finding is often corrupted by poor image quality. Here a method to integrate the two approaches is proposed using an estimation of the maximum a posteriori (MAP) segmentation in an effort to form a unified approach that is robust to noise and poor edges. We further propose to improve the accuracy of the resulting boundary location and update of the snake topology. A number of experiments are performed on both synthetic and LANDSAT 7 images to evaluate the approach.

Combination of imagery - a study on various methods

This paper addresses the problem of image combination. The mathematical model of a general image combination (GIC) method is driven from the physical principle of image formation. It is shown that many existing image combination methods are the particular cases of the GIC method. The performance of various image combination methods is then analyzed based on the advantages and disadvantages of different assumptions.

Sociological aspects of natural hazards

Every disaster has a significant social impact as it affects the population. People’s vulnerability is directly related to their sociological aspects, which are generally overlooked in disaster management. These socio-economic conditions, such as low income, health concerns, lack of education, poor housing, pregnant women, single-parent families, the elderly, and the very young are critical factors in determining the level of disaster risk. This special issue encompasses various sociological aspects and their consequences due to natural hazards. The paper by Hewitt examines relations between natural hazards and social conditions in disasters, and problems with their integration into disaster management. Social analyses suggest the scope of today’s disasters follows primarily from greater concentrations of vulnerable people, exposed in dangerous situations, and lacking adequate protection. A view of disaster causality emerges emphasizing avoidable failures of preventive, protective, and intervention measures. The case for greater attention to issues of governance and social justice is emphasized. The second paper by Armenakis and Nirupama investigates the determination of vulnerability indicators based on GIS-derived spatial and attribute data for hazard and evacuation zones, followed by quantitative spatial risk estimation and ranking. For the determination of disaster risk, the overall community vulnerability was evaluated by combining social, economic, physical, and environmental vulnerabilities. This paper uses a propane explosion incident as the case in point to demonstrate the methodology and procedure for risk using GIS modeling techniques. The effects of extreme weather on economic well-being in rural Mozambique are analyzed by Matyas and Silva. Rainfall anomalies were established for a 12-year rainfall period using satellite imagery and GIS. They also approximate storm–total rainfall from tropical cyclones entering the Mozambique Channel. They indicate that the impact of receiving abovenormal rainfall may hinder economic well-being more than below-normal rainfall. The study also identifies patterns in sub-national rainfall variability and economic well-being, thus enabling a more detailed understanding of weather-related effects on socio-economic outcomes. The paper by Nirupama studies the risk perception and the importance of

Slobodan P. Simonović: Systems approach to management of disasters: methods and applications

Disaster management deals with decisions and resources allocation in order to provide solutions for hazard mitigation, preparation, emergency response and recovery. Decision making in disaster management is a complex process and involves interdisciplinary teams trying to address and cope with multi-faceted uncertain and high variable activities. This book introduces the use of a systems approach and tools to deal with the complexity of disaster management. It provides the basis for the development of structured approaches using mathematical models and analytical processes. It builds on the author’s substantial experience in the application of systems analysis in the area of water resources management. The book focuses on systems simulation, optimisation through linear programming and multi-objective decision analysis as the main sets of tools for disaster management. The main idea is how we can reach an optimum decision given a number of constraints under variant and ambiguous conditions and therefore have a quantitative basis for decision making. The book is organised into four parts and eight chapters. Part I is an introduction to the integrated disaster management activities and the elements of complexity and uncertainty. It includes the author’s personal experience from the Red River flooding in Manitoba, Canada, and a brief overview of the disaster management procedures in Canada. Part II introduces the systems approach to disaster management, including systems definitions and the systems tools of simulation, optimisation and multi-objective analysis. The material presented in this section relates to the design of a disaster management problem as a systems problem and to the selection of appropriate tools for its solution. The theoretical details of simulation, optimisation and multi-objective analysis along with their practical application in disaster management are given in Part III. The development of a systems dynamics simulation is demonstrated with examples. The widely used linear programming technique is discussed in the context of disaster optimisation problems. For the multi-objective analysis, a very practical approach is used to demonstrate the