OBIA based hierarchical image classification for industrial lake water

Avci, Z. Damla Uça; Karaman, Muhittin; Özelkan, Emre; Kumral, Mustafa; Budakoǧlu, Murat

doi:10.1016/j.scitotenv.2014.04.048

Cited by 13 publications

(6 citation statements)

References 12 publications

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“…In the multi resolution segmentation process, the input parameters scale and homogeneity namely compactness and shape were used. This approach is carried out by starting with segmentation or separation of objects (Labib & Harris, 2018;Sari & Kushardono, 2016;Uca Avci et al, 2014). After doing this step, the vegetation and non-vegetation classes will be generated.…”

Section: Methodsmentioning

confidence: 99%

Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery

Sari

Rokhmatuloh

Manessa

2022

Geoplanning: Journal of Geomatics and Planning

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The existence of vegetation in an area has an important role to maintain the carrying capacity of the environment and create a comfortable environment as a place to live. In an effort to create a sustainable environment, there are various pressures on vegetation that cause a decrease in vegetation area. Economic activity, population growth and other anthropogenic activities trigger the dynamics of vegetation cover in an area that causes land cover changes from vegetation to non-vegetation. Majalengka Regency as one of the areas with intensive regional physical development in line with the operation of BIJB Kertajati and the Cipali toll road became the study area in this research. This study aims to monitor the dynamics of vegetation cover with the proposed method namely the integration of the OBIA and Random Forest classifier using multi temporal Sentinel-2 satellite imagery. The results show that there is a decrease in the area of vegetation in the research area as much as 4,329.6 hectares to non-vegetation areas in the period 2016-2020. The vegetation area in 2020 is 84,716.07 hectares and non-vegetation area is 35,708 hectares. Thus, there has been a decrease in the percentage of vegetation area from 73.94% in 2016 to 70.35% in 2020, meanwhile for non-vegetation areas there has been an increase from 26.06% in 2016 to 29.65% in 2020.

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Section: Methodsmentioning

confidence: 99%

Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery

Sari

Rokhmatuloh

Manessa

2022

Geoplanning: Journal of Geomatics and Planning

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“…Estos estudios usaron técnicas basadas en análisis de tonos y luminosidad de los niveles digitales. Se centran en los valores espectrales y en su mayoría ignoran el contexto de vecindad (Li, Zang, Zhang, Li & Wu, 2014;Uca Avci et al, 2014).…”

Section: Revista Perspectiva Geográficaunclassified

“…Este método permite modelar la superficie terrestre mediante el análisis espacial, topológico y jerárquico de los datos. Su principal diferencia con los métodos espectrales hace referencia al cambio en la estructura de los datos a procesar, ya que su unidad de medida no es un pixel, sino una agrupación de pixeles que genera regiones que representan el paisaje (Uca Avci et al, 2014;Ma et al, 2017).…”

Section: Revista Perspectiva Geográficaunclassified

“…El análisis multitemporal de un objeto sobre la superficie terrestre juega un papel importante debido a su uso práctico en aplicaciones como monitoreo forestal (Hansen & Loveland, 2012), monitoreo de desastres naturales (Hedhli, Moser, Zerubia & Serpico, 2014), análisis de la cobertura del suelo urbano (Lizarazo, 2012), estudios geomorfológicos (Ariza, Roa, Serrato, Aicardo & Rincón, 2018) y análisis espaciotemporal en la dinámica de ríos (Rozo, Nogueira & Soto, 2014;Uca Avci et al, 2014;Yang et al, 2015), entre otros.…”

Section: Introductionunclassified

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Análisis multitemporal de la frontera fluvial entre Perú y Colombia mediante análisis de imágenes satelitales basado en objetos geográficos durante el período 1989-2015

2020

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Para este trabajo se propone una metodología semiautomatizada para el estudio de la dinámica fluvial de grandes ríos mediante el análisis multitemporal de imágenes satelitales basado en objetos geográficos (GEOBIA). El método brinda interoperabilidad directa con los sistemas de información geográfica, permite la cuantificación de la variación de las formas en superficie existentes y es eficiente en tiempo de cómputo. Los resultados obtenidos con esta metodología se verifican con datos tomados en campo. La metodología se aplica a la dinámica del río Amazonas, que ha generado conflicto entre Perú y Colombia debido a que el río fue establecido como la línea limítrofe que separa estos dos países. Los resultados muestran las repercusiones en superficie en cuanto a los cambios en las islas de arena, la erosión y la dinámica en el fondo del canal durante el período 1989-2015, causada por la carga de sedimentos y el movimiento del thalweg. En la validación con el estudio de campo se encontró un 99,2% de exactitud entre lo estimado y lo existente.

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“…Technically, the various characteristics in the calculated segments, such as shape, texture, layer-based values, and the context of the object, are considered as the main factors of the classification process in the OBIA technique [23]. One of the most significant characteristics of the OBIA approach is the possibility of detecting and classifying targets bigger than pixels as image objects, which allows for the integration of a variety of spatial and spectral features, such as textural parameters, shape, neighborhood, and relations for modelling tasks [24]. Additionally, OBIA offers the ability of using the intrinsic properties of objects and the use of contextual or spatial behavior through the neighborhood or topological relationships between objects [25].…”

Section: Introductionmentioning

confidence: 99%

Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)

et al. 2021

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This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of multispectral (MS) bands (~2 m) was first improved using a pan-sharpening technique that provides a solution by fusing the information of the panchromatic (PAN) and MS bands to generate pan-sharpened images with a spatial resolution of about 50 cm. After applying a segmentation procedure, the classification step was considered as the main process of extracting the aimed features. The aforementioned classification method includes applying spectral and shape indices. Then, the classes were defined as follows: type 1 (settlement area) was collapsed areas, non-collapsed areas, and camps; type 2 (vegetation area) was orchards, cultivated areas, and urban green spaces; and type 3 (miscellaneous area) was rocks, rivers, and bare lands. As OBIA results in the integration of the spatial characteristics of the image object, we also aimed to evaluate the efficiency of object-based features for damage assessment within the semi-automated approach. For this goal, image context assessment algorithms (e.g., textural parameters, shape, and compactness) together with spectral information (e.g., brightness and standard deviation) were applied within the integrated approach. The classification results were satisfactory when compared with the reference map for collapsed buildings provided by UNITAR (the United Nations Institute for Training and Research). In addition, the number of temporary camps was counted after applying OBIA, indicating that 10,249 tents or temporary shelters were established for homeless people up to 17 November 2018. Based on the total damaged population, the essential resources such as emergency equipment, canned food and water bottles can be estimated. The research makes a significant contribution to the development of remote sensing science by means of applying different object-based image-analyzing techniques and evaluating their efficiency within the semi-automated approach, which, accordingly, supports the efficient application of these methods to other worldwide case studies.

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OBIA based hierarchical image classification for industrial lake water

Cited by 13 publications

References 12 publications

Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery

Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery

Análisis multitemporal de la frontera fluvial entre Perú y Colombia mediante análisis de imágenes satelitales basado en objetos geográficos durante el período 1989-2015

Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)

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