scholarly journals COMPARAÇÃO DE MÉTODOS DE CLASSIFICAÇÃO PARA O MAPEAMENTO DA COBERTURA DA TERRA NO SETOR NORTE DA ILHA DO MARANHÃO

2020 ◽  
Vol 39 (04) ◽  
pp. 1129-1140
Author(s):  
Adilson Matheus Borges MACHADO ◽  
Taíssa Caroline SILVA RODRIGUES
Keyword(s):  
Image Analysis ◽  
Maximum Likelihood ◽  
Maximum Likelihood Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

A zona costeira se estende das planícies costeiras até a borda externa das plataformas continentais. A GEOBIA (Geographic Object-Based Image Analysis) é um método eficaz na análise de imagens de alta resolução, incluindo fotointerpretação e classificação de características da paisagem. O método de classificação supervisionada consiste em um conhecimento prévio de algumas áreas a serem estudadas, o que permite amostras de confiáveis. Assim, o presente estudo tem como objetivo principal comparar o método de classificação supervisionada pixel a pixel com o método de GEOBIA, buscando o mapeamento da cobertura da terra no setor Norte da Ilha do Maranhão. O algoritmo classificador utilizado foi o Maximum Likelihood Classification. O método de Classificação Supervisionada apresentou um valor de Kappa de 0,8469, Exatidão Global de 0,8950 e Variância Kappa de 0,0009295. Para a GEOBIA apresentou um valor de Kappa de 0,9410, Exatidão Global de 0,9600 e Variância Kappa de 0,0004075. O sensoriamento remoto se mostra extremamente eficiente em pesquisas relacionadas ao uso e ocupação da terra. Os resultados do método da Classificação Supervisionada, apesar de suas limitações, mostraram resultados satisfatórios quando comparados com os resultados do método da GEOBIA, o que torna interessante sua aplicação para iniciativas de mapeamento em diversos municípios brasileiros.

scholarly journals OBJECT-BASED IMAGE ANALYSIS FOR HISTORIC MAPS CLASSIFICATION

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 247-254 ◽  
Author(s):  
P. Zatelli ◽  
S. Gobbi ◽  
C. Tattoni ◽  
N. La Porta ◽  
M. Ciolli
Keyword(s):  
Image Analysis ◽  
Maximum Likelihood ◽  
Confusion Matrix ◽  
Added Value ◽  
Maximum Likelihood Classification ◽  
Grass Gis ◽  
Object Based Image Analysis ◽  
The North ◽  
Object Based

<p><strong>Abstract.</strong> Heritage maps represent fundamental information for the study of the evolution of a region, especially in terms of landscape and ecologic features. Historical maps present two kinds of hurdle before they can be used in a modern GIS: they must be geometrically corrected to correspond to the datum in use and they must be classified to exploit the information they contain. This study deals the latter problem: the Historical Cadaster Map, created between 1851 and 1861, for the Trentino region in the North of Italy is available as a collection of maps in the ETRS89/UTM 32N datum. The map is a high resolution scan (230 DPI, 24 bit) of the original map and has been used in several ecological studies, since it provides detailed information not only about land property but also about land use. In the past the cadaster map has been manually digitized and for each area a set of attributes has been recorded. Since this approach is time consuming and prone to errors, automatic and semi-automatic procedures have been tested. Traditional image classification techniques, such as maximum likelihood classification, supervised or un-supervised, pixelwise and contextual, do not provide satisfactory results for many reasons: map colors are very variable within the same area, symbols and characters are used to identify cadaster parcels and locations, lines, drawn by hand on the original map, have variable thickness and colors. The availability of FOSS tools for the Object-based Image Analysis (OBIA) has made possible the application of this technique to the cadaster map. This paper describes the use of GRASS GIS and R for the implementation of the OBIA approach for the supervised classification of the historic cadaster map. It describes the determination of the optimal segments, the choice of their attributes and relevant statistics, and their classification. The result has been evaluated with respect to a manually digitized map using Cohens Kappa and the analysis of the confusion matrix. The result of the OBIA classification has also been compared to the classification of the same map using maximum likelihood classification, un-supervised and supervised, both pixelwise and contextual. The OBIA approach has provided very satisfactory results with the ability to automatically remove the background and symbols and characters, creating a ready to be used classified map. This study highlights the effectiveness of the OBIA processing chain available in the FOSS4G ecosystem, and in particular the added value of the interoperability between GRASS GIS and R.</p>

scholarly journals Avaliação de desempenho de atributos estatísticos e texturais em uma classificação de cobertura da terra baseada em objeto

2012 ◽  
Vol 18 (2) ◽  
pp. 302-326 ◽  
Author(s):  
Cristiane Nunes Francisco ◽  
Cláudia Maria de Almeida
Keyword(s):  
Image Analysis ◽  
Rio De Janeiro ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Alos Prism

Este artigo tem como objetivo avaliar o desempenho de duas redes semânticas geradas por mineração de dados para a classificação de cobertura da terra por meio de análise de imagens baseada em objetos geográficos (GEographic Object-Based Image Analysis - GEOBIA). Para isto, uma rede utilizou-se de descritores estatísticos e texturais, e a outra, apenas de descritores estatísticos. A base de dados foi constituída de imagens ALOS/AVNIR fusionadas com imagens ALOS/PRISM e dados de relevo provenientes do banco de dados TOPODATA. A área de estudo corresponde ao município de Nova Friburgo, com 933 km², localizado na região serrana do estado do Rio de Janeiro. O índice Kappa alcançado pela classificação baseada em árvore de decisão composta por descritores estatísticos e texturais foi de 0,81, enquanto que este valor para a classificação derivada apenas de descritores estatísticos foi de 0,84. Considerando os índices alcançados, conclui-se que ambos os resultados apresentam excelente qualidade quanto à acurácia da classificação. O teste de hipótese entre os dois índices mostra, com nível de significância de 5%, que não há diferenças entre as duas classificações quanto à acurácia.

scholarly journals Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

2019 ◽  
Vol 8 (12) ◽  
pp. 551 ◽  
Author(s):  
Raphael Knevels ◽  
Helene Petschko ◽  
Philip Leopold ◽  
Alexander Brenning
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Land Surface ◽  
Region Growing ◽  
Support Vector ◽  
Visual Interpretation ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Gis Software

With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (κ = 0.42) in the identification of landslides.

scholarly journals Geographic Object Based Image Analysis of WorldView-3 Imagery for Urban Hydrologic Modelling at the Catchment Scale

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1133 ◽  
Author(s):  
Mark Randall ◽  
Rasmus Fensholt ◽  
Yongyong Zhang ◽  
Marina Bergen Jensen
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Classification ◽  
Supplementary Data ◽  
Catchment Scale ◽  
Hydrologic Modelling ◽  
Sponge City ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

China’s Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation.

scholarly journals Geographic Object-Based Image Analysis: A Primer and Future Directions

2020 ◽  
Vol 12 (12) ◽  
pp. 2012 ◽  
Author(s):  
Maja Kucharczyk ◽  
Geoffrey J. Hay ◽  
Salar Ghaffarian ◽  
Chris H. Hugenholtz
Keyword(s):  
Image Analysis ◽  
Current Status ◽  
Future Research ◽  
Deep Convolutional Neural Networks ◽  
Comprehensive Overview ◽  
Image Objects ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Methodological Considerations

Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial); (ii) best practices informed by research; and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.

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