Identification and classification of geographically isolated wetlands in North Alabama using geographic object based image analysis (GeOBIA)

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.

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Geographic Object Based Image Analysis (GEOBIA) for Mangrove Canopy Delineation Using Aerial Photography

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/313/1/012048 ◽

2019 ◽

Vol 313 ◽

pp. 012048

Author(s):

M Kamal ◽

T Kanekaputra ◽

R Hermayani ◽

A Juniansah

Keyword(s):

Image Analysis ◽

Aerial Photography ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8120551 ◽

2019 ◽

Vol 8 (12) ◽

pp. 551 ◽

Cited By ~ 3

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.

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

Water ◽

10.3390/w11061133 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1133 ◽

Cited By ~ 6

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.

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

Remote Sensing ◽

10.3390/rs12122012 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2012 ◽

Cited By ~ 3

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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