National fuel type mapping methodology using geographic object based image analysis and landsat 8 oli imagery

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy.

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Winter Wheat Mapping Using Landsat 8 Images and Geographic Object-Based Image Analysis

Transactions of the ASABE ◽

10.13031/trans.11933 ◽

2017 ◽

Vol 60 (3) ◽

pp. 625-633

Author(s):

Tengfei Su ◽

Shengwei Zhang

Keyword(s):

Image Analysis ◽

Winter Wheat ◽

Feature Reduction ◽

Classification Model ◽

Feature Analysis ◽

Landsat 8 ◽

Scale Selection ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object

Abstract. Winter wheat is a major food source in many areas, so it is necessary to construct an effective approach for its monitoring based on satellite data. By taking advantage of geographic object-based image analysis (GEOBIA), a winter wheat classification framework was established. Two stages, which included scale selection and feature analysis, were incorporated into the new approach. The scale selection stage was implemented based on an unsupervised method, so human intervention for tuning the scale parameter of image segmentation can be largely saved. The feature analysis stage was performed on the basis of a random forest classification model, and in the experiment this step allowed for feature reduction, which was validated to be beneficial to the classification performance. Keywords: Feature analysis, GEOBIA, Scale selection, Winter wheat.

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Classificação orientada a objeto (GEOBIA) aplicada à análise das alterações do uso e cobertura da terra da Bacia Hidrográfica do Rio da Prata em Jardim e Bonito - MS

GEOGRAFIA (Londrina) ◽

10.5433/2447-1747.2017v26n1p05 ◽

2017 ◽

Vol 26 (1) ◽

pp. 05

Author(s):

Laís Coêlho do Nascimento Silva ◽

Vitor Matheus Bacani Matheus Bacani

Keyword(s):

Image Analysis ◽

Thematic Mapper ◽

Landsat 8 ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object

O presente estudo tem como objetivo analisar as mudanças no uso e cobertura da terra da bacia hidrográfica do Rio da Prata, MS, no período de 1986 a 2016, a partir da abordagem de classificação orientada a objeto (GEOBIA - Geographic Object-Based Image Analysis). Utilizaram-se as imagens Landsat 5, sensor TM (Thematic Mapper) para os anos de 1986, 1996 e 2006 e Landsat 8, sensor OLI (Operational Land Imager) para o ano de 2016. Aplicando técnicas de sensoriamento remoto e geoprocessamento, utilizou-se o software Envi 5.1 para a correção radiométrica e atmosférica e para classificação orientada a objeto: o Ecogntion 9, além do Arcgis 10.3. Foram definidas sete classes de uso da terra: agricultura, corpos aquosos, florestal, pastagem, áreas úmidas, vegetação arbórea e reflorestamento. Os resultados indicaram uma expansão agropecuária e consequentemente um decréscimo das áreas de vegetação arbórea. No período de 1986 a 2016, a pastagem teve um aumento de 13,11%, já as áreas de vegetação arbórea tiveram uma supressão de 21,37%. Sendo assim, destaca-se a importância da análise do uso da terra, com auxílio do sensoriamento remoto e geoprocessamento, para indicar estratégias para ordenamento ambiental na bacia do Rio da Prata, MS.

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Fuel type mapping in a typical Mediterranean ecosystem using object-based image analysis of Sentinel 2 imagery and auxiliary GIS data

10.5194/egusphere-egu21-11355 ◽

2021 ◽

Author(s):

Konstantinos Karystinakis ◽

Vasileios Alexandridis ◽

Stefanos Stefanidis ◽

Georgia Kalantzi

Keyword(s):

Image Analysis ◽

Land Cover ◽

Mediterranean Ecosystem ◽

Fuel Type ◽

Development Fund ◽

Object Based Image Analysis ◽

Type Mapping ◽

Object Based ◽

Gis Data ◽

The Mediterranean

<p>Wildfires have been an integral part of the Mediterranean ecosystem. Moreover, the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report emphasizes that the Mediterranean basin is expected to be drier by the end of the 21st century, while future warming will possibly be higher than the global mean. Therefore, outbreaks of wildfires are expected to increase. One of the most important factors for wildfire behavior apart from the meteorological conditions, is fuel types. In this study, a detailed fuel type mapping in a case study area was addressed. To accomplish this goal, an object-based image analysis (OBIA) approach was implemented using the open-source Orfeo toolbox. The freely available Sentinel-2A satellite images were processed in combination with auxiliary European and National scale GIS data. The classification results demonstrate a high-quality Land Cover map with 84% of overall accuracy. The classified land cover polygons were associated with high-resolution tree cover density data derived from Copernicus Land Monitoring Service. This coupling led to the synthesis of the fuel type map. To this end, this approach can fulfill the efficient mapping of fuel types for operational purposes. This research has been co&#8208;financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH &#8211;CREATE &#8211;INNOVATE&#160; (project code:T2EDK-01967)</p>

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Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2013.05.003 ◽

2013 ◽

Vol 82 ◽

pp. 125-137 ◽

Cited By ~ 107

Author(s):

Damien Arvor ◽

Laurent Durieux ◽

Samuel Andrés ◽

Marie-Angélique Laporte

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object

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

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702012000200008 ◽

2012 ◽

Vol 18 (2) ◽

pp. 302-326 ◽

Cited By ~ 6

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.

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