scholarly journals SEGMENTAÇÃO MULTIRRESOLUÇÃO E CLASSIFICAÇÃO ORIENTADA A OBJETOS APLICADOS A IMAGENS SPOT-5 PARA O MAPEAMENTO DO USO DA TERRA

FLORESTA ◽  
2010 ◽  
Vol 40 (2) ◽  
Author(s):  
Naíssa Batista da Luz ◽  
Alzir Felippe Buffara Antunes ◽  
João Batista Tavares Júnior
Keyword(s):  
Land Use ◽  
Fuzzy Logic ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Object Oriented ◽  
Membership Functions ◽  
Multivariate Statistic ◽  
Spot 5 ◽  
Land Use Maps ◽  
High Spatial Resolution Imagery

A abordagem de classificação orientada a objetos representa um novo paradigma no processamento de imagens de alta resolução espacial. A utilização de descritores espectrais e de forma, oriundos da segmentação, permitem uma melhor discriminação seletiva entre os objetos. Funções de pertinência fuzzy podem ser construídas a partir das propriedades dos objetos segmentados. Atualmente, o estado do Paraná vem realizando atualização dos mapas de uso da terra em escala 1:50.000 por meio de ortorretificação de imagens Spot 5. Pretende-se neste trabalho elaborar o mapa de uso da terra por meio de técnicas de segmentação multiresolução e classificação contextualizada (lógica fuzzy). Descritores dos objetos foram selecionados por estatística multivariada, métodos das componentes principais e de discriminantes, determinando-se aqueles com maior potencial de separabilidade entre as classes. Testes de classificação sucessivos foram realizados aplicando-se funções de pertinência fuzzy aos descritores selecionados, procedendo-se à classificação final da imagem. O mapa de uso da terra, abrangendo uma área de aproximadamente 218,75 km2, resultou em um valor de acurácia Kappa em torno de 80% (utilizando-se os objetos selecionados como amostras de treinamento), demonstrando o potencial dessa ferramenta, embora posteriores adaptações metodológicas devam ser implementadas. Palavras-chave: Imagem de alta resolução espacial; lógica fuzzy; hierarquia de classes; rede semântica.   Abstract Multiresolution segmentation, object-oriented classification and Spot-5 imagery land use mapping. The object oriented classification approach represents a new paradigm to the high spatial resolution imagery processing. The use of spectral and form properties originated from the segmentation procedure allows better discrimination between objects. Fuzzy membership functions are generated from the segmented objects descriptors. The State of Parana has been currently updating its 1:50.000 land use maps by means of Spot 5 orthorectified imagery. The objective of this paper is to develop a methodology to the elaboration of land use maps by means of multi-resolution segmentation techniques and image contextual classification with the aid of fuzzy logic. In order to identify which descriptors could provide better class separability, multivariate statistic, principal components and discriminant analysis techniques were used, as a result potential descriptors were selected. Finally the classification process was achieved using those descriptors to create the fuzzy sets and the membership functions. The Land Use Map generated, including an area of 218,75 km2, reached a Kappa index near to 80%, indicating the potential application of this technique nevertheless subsequent methodological adaptation might be implemented.Keywords: High spatial resolution imagery; object oriented classification; fuzzy logic; land use mapping.

scholarly journals Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
Jefferson Francisco Soares ◽  
Gláucia Miranda Ramirez ◽  
Mirléia Aparecida de Carvalho ◽  
Marcelo de Carvalho Alves ◽  
Christiany Mattioli Sarmiento ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Nearest Neighbor ◽  
High Spatial Resolution ◽  
Object Oriented ◽  
Visual Object ◽  
Hydroelectric Power ◽  
K Nearest Neighbor ◽  
High Spatial Resolution Image ◽  
Resolution Image

The maintenance of riparian forests is considered one of the main vegetative practices for mitigating the degradation of water resources and is mandatory by law. However, in Brazil there is still a progressive and constant decharacterization of these areas. Facing this reality, it is necessary to broaden researches that identify the occurring changes and provide efficient solutions at a fast pace and low cost. Remote sensing techniques show great application potential in characterizing natural resources. The objective of this work was to map, to characterize the land use and occupation and to verify the best method of high spatial resolution image classification of the Permanent Preservation Areas of the Funil Hydroelectric Power Plant reservoir, located between the municipalities of Lavras, Perdões, Bom Sucesso, Ibituruna, Ijací and Itumirim, in the state of Minas Gerais. The methods used to classify the high spatial resolution image from the Quickbird satellite were visual, object-oriented and pixel-by-pixel. Results showed the best method for mapping land use and occupation of the study area was object-oriented classification using the K-nearest neighbor algorithm, with kappa coefficient of 0.88 and global accuracy of 91.40%.

Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery

2011 ◽  
Vol 25 (6) ◽  
pp. 1025-1043 ◽  
Author(s):  
Eva Savina Malinverni ◽  
Anna Nora Tassetti ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Emanuele Frontoni ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Object Based ◽  
High Spatial Resolution Imagery

Combination of GF-2 high spatial resolution imagery and land surface factors for predicting soil salinity of muddy coasts

CATENA ◽  
2021 ◽  
Vol 202 ◽  
pp. 105304
Author(s):  
Yufeng Li ◽  
Cheng Wang ◽  
Alan Wright ◽  
Hongyu Liu ◽  
Huabing Zhang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Soil Salinity ◽  
Land Surface ◽  
High Spatial Resolution ◽  
High Spatial Resolution Imagery

scholarly journals Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Points Of Interest ◽  
Very High Spatial Resolution ◽  
Functional Zones ◽  
Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

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