Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Development of an Autonomous Aerial Toolset for Agricultural Applications

According to the United Nations, the world population is expected to grow from its current 7 billion to 9.7 billion by the year 2050. During this time, global food demand is also expected to increase by between 59% and 98% due to the population increase, accompanied by an increasing demand for protein due to a rising standard of living throughout developing countries. [1] Meeting this increase in required food production using present agricultural practices would necessitate a similar increase in farmland; a resource which does not exist in abundance. Therefore, in order to meet growing food demands, new methods will need to be developed to increase the efficiency of farming, thereby increasing yield from the present land. One way in which this problem can be solved is through the usage of autonomous aerial systems to scout for problems which could potentially affect the crop yield – such as nutrient deficiency, water stress, or diseases. Once located, this data can be used to determine the proper treatment for the field to alleviate the problem. Through this process, resources can be reduced to the required minimum, while problems affecting the crop yield will still be corrected, allowing greater production with a lower amount of resources. This project on the application of Unmanned Aerial Vehicles (UAV’s) to the field of agriculture consisted of two phases. First, a study was conducted on the required background to define the problem statement and what solutions were available for this application. This consisted of first defining the operations within agriculture where UAV’s could be used to increase efficiency, and then the sensors, hardware, and software these operations would require. The remainder of the project consisted of evaluating the tools which could be utilized to develop such a solution. Primarily, the project focused on software tools – programming software, simulation environments, and machine learning algorithms – which could be utilized by future students to develop a functional hardware and software toolchain for the research of autonomous systems for agricultural applications. After analyzing these development solutions, a set of tools was selected which showed promise in the creation of a functional solution. It was demonstrated that the core functions required for a UAV-based agricultural solution – navigation, perception, and feature detection – could be implemented within these systems, implying that they could be integrated into a full solution. As the tools were selected to ensure the developed algorithms would be transferable to physical platforms, this additionally supports a physical system could also be developed. The present work is part of the Autonomous Systems Lab which belongs to the WKU Center for Energy Systems. The author hopes that this project contributes to the advancement of the curriculum within the engineering department and serves as a foundation for future students developing autonomous systems, perception, and applied artificial intelligence at WKU

Context based detection of urban land use zones

This dissertation proposes an automated land-use zoning system based on the context of an urban scene. Automated zoning is an important step toward improving object extraction in an urban scene

Automated methods for image detection of cultural heritage: Overviews and perspectives

Remote sensing data covering large geographical areas can be easily accessed and are being acquired with greater frequency. The massive volume of data requires an automated image analysis system. By taking advantage of the increasing availability of data using computer vision, we can design specific systems to automate data analysis and detection of archaeological objects. In the past decade, there has been a rise in the use of automated methods to assist in the identification of archaeological sites in remote sensing imagery. These applications offer an important contribution to non-intrusive archaeological exploration, helping to reduce the traditional human workload and time by signalling areas with a higher probability of presenting archaeological sites for exploration. This survey describes the state of the art of existing automated image analysis methods in archaeology and highlights the improvements thus achieved in the detection of archaeological monuments and areas of interest in landscape-scale satellite and aerial imagery. It also presents a discussion of the benefits and limitations of automatic detection of archaeological structures, proposing new approaches and possibilities.info:eu-repo/semantics/publishedVersio

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

A methodology to produce geographical information for land planning using very-high resolution images

Actualmente, os municípios são obrigados a produzir, no âmbito da elaboração dos instrumentos de gestão territorial, cartografia homologada pela autoridade nacional. O Plano Director Municipal (PDM) tem um período de vigência de 10 anos. Porém, no que diz respeito à cartografia para estes planos, principalmente em municípios onde a pressão urbanística é elevada, esta periodicidade não é compatível com a dinâmica de alteração de uso do solo. Emerge assim, a necessidade de um processo de produção mais eficaz, que permita a obtenção de uma nova cartografia de base e temática mais frequentemente. Em Portugal recorre-se à fotografia aérea como informação de base para a produção de cartografia de grande escala. Por um lado, embora este suporte de informação resulte em mapas bastante rigorosos e detalhados, a sua produção têm custos muito elevados e consomem muito tempo. As imagens de satélite de muito alta-resolução espacial podem constituir uma alternativa, mas sem substituir as fotografias aéreas na produção de cartografia temática, a grande escala. O tema da tese trata assim da satisfação das necessidades municipais em informação geográfica actualizada. Para melhor conhecer o valor e utilidade desta informação, realizou-se um inquérito aos municípios Portugueses. Este passo foi essencial para avaliar a pertinência e a utilidade da introdução de imagens de satélite de muito alta-resolução espacial na cadeia de procedimentos de actualização de alguns temas, quer na cartografia de base quer na cartografia temática. A abordagem proposta para solução do problema identificado baseia-se no uso de imagens de satélite e outros dados digitais em ambiente de Sistemas de Informação Geográfica. A experimentação teve como objectivo a extracção automática de elementos de interesse municipal a partir de imagens de muito alta-resolução espacial (fotografias aéreas ortorectificadas, imagem QuickBird, e imagem IKONOS), bem como de dados altimétricos (dados LiDAR). Avaliaram-se as potencialidades da informação geográfica extraídas das imagens para fins cartográficos e analíticos. Desenvolveram-se quatro casos de estudo que reflectem diferentes usos para os dados geográficos a nível municipal, e que traduzem aplicações com exigências diferentes. No primeiro caso de estudo, propõe-se uma metodologia para actualização periódica de cartografia a grande escala, que faz uso de fotografias aéreas vi ortorectificadas na área da Alta de Lisboa. Esta é uma aplicação quantitativa onde as qualidades posicionais e geométricas dos elementos extraídos são mais exigentes. No segundo caso de estudo, criou-se um sistema de alarme para áreas potencialmente alteradas, com recurso a uma imagem QuickBird e dados LiDAR, no Bairro da Madre de Deus, com objectivo de auxiliar a actualização de cartografia de grande escala. No terceiro caso de estudo avaliou-se o potencial solar de topos de edifícios nas Avenidas Novas, com recurso a dados LiDAR. No quarto caso de estudo, propõe-se uma série de indicadores municipais de monitorização territorial, obtidos pelo processamento de uma imagem IKONOS que cobre toda a área do concelho de Lisboa. Esta é uma aplicação com fins analíticos onde a qualidade temática da extracção é mais relevante.Currently, the Portuguese municipalities are required to produce homologated cartography, under the Territorial Management Instruments framework. The Municipal Master Plan (PDM) has to be revised every 10 years, as well as the topographic and thematic maps that describe the municipal territory. However, this period is inadequate for representing counties where urban pressure is high, and where the changes in the land use are very dynamic. Consequently, emerges the need for a more efficient mapping process, allowing obtaining recent geographic information more often. Several countries, including Portugal, continue to use aerial photography for large-scale mapping. Although this data enables highly accurate maps, its acquisition and visual interpretation are very costly and time consuming. Very-High Resolution (VHR) satellite imagery can be an alternative data source, without replacing the aerial images, for producing large-scale thematic cartography. The focus of the thesis is the demand for updated geographic information in the land planning process. To better understand the value and usefulness of this information, a survey of all Portuguese municipalities was carried out. This step was essential for assessing the relevance and usefulness of the introduction of VHR satellite imagery in the chain of procedures for updating land information. The proposed methodology is based on the use of VHR satellite imagery, and other digital data, in a Geographic Information Systems (GIS) environment. Different algorithms for feature extraction that take into account the variation in texture, color and shape of objects in the image, were tested. The trials aimed for automatic extraction of features of municipal interest, based on aerial and satellite high-resolution (orthophotos, QuickBird and IKONOS imagery) as well as elevation data (altimetric information and LiDAR data). To evaluate the potential of geographic information extracted from VHR images, two areas of application were identified: mapping and analytical purposes. Four case studies that reflect different uses of geographic data at the municipal level, with different accuracy requirements, were considered. The first case study presents a methodology for periodic updating of large-scale maps based on orthophotos, in the area of Alta de Lisboa. This is a situation where the positional and geometric accuracy of the extracted information are more demanding, since technical mapping standards must be complied. In the second case study, an alarm system that indicates the location of potential changes in building areas, using a QuickBird image and LiDAR data, was developed for the area of Bairro da Madre de Deus. The goal of the system is to assist the updating of large scale mapping, providing a layer that can be used by the municipal technicians as the basis for manual editing. In the third case study, the analysis of the most suitable roof-tops for installing solar systems, using LiDAR data, was performed in the area of Avenidas Novas. A set of urban environment indicators obtained from VHR imagery is presented. The concept is demonstrated for the entire city of Lisbon, through IKONOS imagery processing. In this analytical application, the positional quality issue of extraction is less relevant.GEOSAT – Methodologies to extract large scale GEOgraphical information from very high resolution SATellite images (PTDC/GEO/64826/2006), e-GEO – Centro de Estudos de Geografia e Planeamento Regional, da Faculdade de Ciências Sociais e Humanas, no quadro do Grupo de Investigação Modelação Geográfica, Cidades e Ordenamento do Territóri

Discriminant Analysis with Spatial Weights for Urban Land Cover Classification

Classifying urban area images is challenging because of the heterogeneous nature of the urban landscape resulting in mixed pixels and classes with highly variable spectral ranges. Approaches using ancillary data, such as knowledge based or expert systems, have shown to improve the classification accuracy in urban areas. Appropriate ancillary data, however, may not always be available. The goal of this study is to compare the results of the discriminant analysis statistical technique with discriminant analysis with spatial weights to classify urban land cover. Discriminant analysis is a statistical technique used to predict group membership for a target based on the linear combination of independent variables. Strict per pixel statistical analysis however does not consider the spatial dependencies among neighbouring pixels. Our study shows that approaches using ancillary data continue to outperform strict spectral classifiers but that using a spatial weight improved the results. Furthermore, results show that when the discriminant analysis technique works well then the spatially weighted approach performs better. However, when the discriminant analysis performs poorly, those poor results are magnified in the spatially weighted approach in the same study area. The study shows that for dominant classes, adding spatial weights improves the classification accuracy.

Object-based mapping of temperate marine habitats from multi-resolution remote sensing data

PhD ThesisHabitat maps are needed to inform marine spatial planning but current methods of field survey and data interpretation are time-consuming and subjective. Object-based image analysis (OBIA) and remote sensing could deliver objective, cost-effective solutions informed by ecological knowledge. OBIA enables development of automated workflows to segment imagery, creating ecologically meaningful objects which are then classified based on spectral or geometric properties, relationships to other objects and contextual data. Successfully applied to terrestrial and tropical marine habitats for over a decade, turbidity and lack of suitable remotely sensed data had limited OBIA’s use in temperate seas to date. This thesis evaluates the potential of OBIA and remote sensing to inform designation, management and monitoring of temperate Marine Protected Areas (MPAs) through four studies conducted in English North Sea MPAs. An initial study developed OBIA workflows to produce circalittoral habitat maps from acoustic data using sequential threshold-based and nearest neighbour classifications. These methods produced accurate substratum maps over large areas but could not reliably predict distribution of species communities from purely physical data under largely homogeneous environmental conditions. OBIA methods were then tested in an intertidal MPA with fine-scale habitat heterogeneity using high resolution imagery collected by unmanned aerial vehicle. Topographic models were created from the imagery using photogrammetry. Validation of these models through comparison with ground truth measurements showed high vertical accuracy and the ability to detect decimetre-scale features. The topographic and spectral layers were interpreted simultaneously using OBIA, producing habitat maps at two thematic scales. Classifier comparison showed that Random Forests Abstract ii outperformed the nearest neighbour approach, while a knowledge-based rule set produced accurate results but requires further research to improve reproducibility. The final study applied OBIA methods to aerial and LiDAR time-series, demonstrating that despite considerable variability in the data, pre- and post-classification change detection methods had sufficient accuracy to monitor deviation from a background level of natural environmental fluctuation. This thesis demonstrates the potential of OBIA and remote sensing for large-scale rapid assessment, detailed surveillance and change detection, providing insight to inform choice of classifier, sampling protocol and thematic scale which should aid wider adoption of these methods in temperate MPAs.Natural Environment Research Council and Natural Englan