Extração De Feições Urbanas Em Imagens De Alta Resolução Espacial a Partir Do Estudo Do Comportamento Espectral Dos Alvos

Pinho, Carolina Moutinho Duque de; Ummus, Marta Eichemberger; Novack, Tessio

doi:10.14393/rbcv63n4-49213

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…To define the Cover/ Soil class, the difficulty in separating objects belonging to buildings with ceramic tile roofs from objects with the characteristics of open land in a medium tone, was taken into consideration. According to Pinho et al (2011) these two classes have very similar makeups which can cause classification confusion.…”

Section: Thematic Map T Initial Preparationmentioning

confidence: 99%

High Spatial Resolution Images for Monthly Pipeline Monitoring

Marques

Ferreira

Riedel

et al. 2013

Rev. Bras. Cartogr.

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O objetivo do estudo foi propor um procedimento de detecção de mudanças, a partir do mapeamento da cobertura da terra, para o monitoramento mensal de faixa de dutos, com imagens de alta resolução espacial. Selecionou-se um trecho da faixa de dutos Rio de Janeiro-Belo Horizonte (largura de 400m e 30,7km de extensão), localizado em Duque de Caxias e Nova Iguaçu - RJ. Foram utilizadas seis imagens GeoEye (Janeiro a Junho/2010) e elaborado um mapa inicial de cobertura da terra a partir da interpretação visual da imagem de janeiro. As áreas de mudanças foram identificadas por álgebra multitemporal das bandas do vermelho para cada período (Jan/Fev, Fev/Mar, Mar/Abr, Abr/Mai e Mai/Jun). A cobertura da terra nas áreas de mudança foi classificada utilizando as funções de pertinência fuzzy. Os resultados mostram que, no período de seis meses, detectou-se 775.402m2de área de mudança, em uma área total de 11.890.000m2. As maiores mudanças ocorreram para Solo Exposto, Vegetação Rasteira e Cobertura/Solo Exposto. O acerto geral apresentou-se entre 71 e 80%, demonstrando que o procedimento empregado foi adequado para o monitoramento de faixa de dutos. No entanto, a implementação deste procedimento deve considerar a variação dos parâmetros de aquisição da imagem de alta resolução, bem como explorar sua aplicação em diferentes escalas e contextos geográficos.

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Section: Thematic Map T Initial Preparationmentioning

confidence: 99%

High Spatial Resolution Images for Monthly Pipeline Monitoring

Marques

Ferreira

Riedel

et al. 2013

Rev. Bras. Cartogr.

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Identifying Precarious Settlements and Urban Fabric Typologies Based on GEOBIA and Data Mining in Brazilian Amazon Cities

et al. 2022

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Although 70% of the Amazon population lives in urban areas, studies on the urban Amazon are scarce. Much of the urban Amazon population lives in precarious settlements. The distinctiveness and diversity of Amazonian precarious settlements are vast and must be identified to be considered in the development of appropriate public policies. Aiming at investigating precarious settlements in Amazon, this study is guided by the following questions: For the Brazilian Amazon region, is it possible to identify areas of precarious settlements by combining geoprocessing and remote sensing techniques? Are there different typologies of precarious settlements distinguishable by their spatial arrangements? Thus, we developed a methodology for identifying precarious settlements and subsequently classifying them into urban fabric typologies (UFT), choosing the cities of Altamira, Cametá, and Marabá as study sites. Our classification model utilized geographic objects-based image analysis (GEOBIA) and data mining of spectral data from WPM sensor images from the CBERS-4A satellite, jointly with texture metrics, context metrics, biophysical index, voluntary geographical information, and neighborhood relationships. With the C5.0 decision tree algorithm we carried out variable selection and classification of these geographic objects. Our estimated models show accuracy above 90% when applied to the study sites. Additionally, we described Amazonian UFT in six types to be identified. We concluded that Amazonian precarious settlements are morphologically diverse, with an urban fabric different from those commonly found in Brazilian metropolitan areas. Identifying and characterizing distinct precarious areas is vital for the planning and development of sustainable and effective public policies for the urban Amazon.

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Extração De Feições Urbanas Em Imagens De Alta Resolução Espacial a Partir Do Estudo Do Comportamento Espectral Dos Alvos

Cited by 2 publications

References 3 publications

High Spatial Resolution Images for Monthly Pipeline Monitoring

High Spatial Resolution Images for Monthly Pipeline Monitoring

Identifying Precarious Settlements and Urban Fabric Typologies Based on GEOBIA and Data Mining in Brazilian Amazon Cities

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