Avaliação da acurácia posicional de vértices obtidos por imagem de sensor orbital e aerofotogrametria para fins de georreferenciamento de imóveis rurais

Em sua grande maioria, o georreferenciamento de imóveis rurais tem sido realizado somente com o levantamento geodésico (LG) por meio de receptores GNSS. Porém, é possível realizá-lo por meio de imagens de satélites e imagens aerotransportadas. A utilização de imagens orbitais ou aerotransportadas pode reduzir o tempo de serviço e auxiliar em limites inacessíveis e naturais. O maior problema em realizar o georreferenciamento utilizando imagens está em atender às precisões exigidas pelo Instituto Nacional de Colonização e Reforma Agrária (INCRA), em razão do imageamento ser menos preciso que o levantamento geodésico. Outra dificuldade está em identificar feições que se encontram sob matas. Entretanto, no mercado existem imagens de satélite de alta resolução espacial e também existe a possibilidade de obtenção de imagens coletadas por aeronaves remotamente pilotadas (ARP) com altíssima resolução espacial que podem atender as exigências. Deste modo este trabalho tem como objetivo avaliar as feições obtidas por três imagens, uma WorldView-3, uma PlanetScope e por uma ortofoto de ARP, sendo estas três comparadas e avaliadas a partir do LG por meio de receptores GNSS. Entre os conjuntos de dados utilizados o melhor resultado de acordo com a classificação normativa do INCRA foi a ortofoto gerada pelo levantamento aerofotogramétrico, pois atendeu à precisão para os vértices artificiais, naturais e vértices inacessíveis. No entanto, a imagem WorldView-3 apresentou o pior resultado na classificação, pois não atendeu nenhum dos tipos de vértices. Entre os três conjuntos de dados utilizados recomenda-se utilizar o levantamento aerofotogramétrico para realizar o georreferenciamento de imóveis rurais. Evaluation of the positional accuracy of features obtained by images of orbital sensors and airborne for georeferencing of rural propertiesA B S T R A C TConcerning methods of positioning the georeferencing of rural properties, it stands out the topographical and geodetic surveys. However, it is possible to make through remote sensing (images of orbital sensors and airborne). The use of orbital or air-bone images can reduce service time and help in inaccessible areas, such as unreachable and natural limits. The most significant difficulty of the georeferencing using images is to meet the required accuracy by the National Institute of Colonization and Agrarian Reform (INCRA). However, there are high spatial resolution satellite images are now available. There is the possibility of getting the images collected by remotely piloted aircraft (RPA) with a very high spatial resolution that meets the requirements. This work aims to assess the features obtained by three images, a WorldView-3, a Planet Scope, and an RPA orthophoto. These three are being compared and evaluated from a geodetic survey and subsequently classified according to the cartographic precision standard of INCRA. The best dataset for the normative of INCRA was the orthophoto generated by RPA because it met the precision for artificial, natural vertices and inaccessible vertices. However, the WV-3 image had the worst result in the classification because it did not meet consistent accuracy to any of the vertices' types. Between the three data sets used, the one that best suits the specifications of georeferencing of rural properties were the images airborne.Key words: Remote Sensing, INCRA Rules, Aerophotogrammetric Survey, Cartographic Accuracy Standard.

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

Remote Sensing ◽

10.3390/rs10111737 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1737 ◽

Cited By ~ 19

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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OSIS: remote sensing code for estimating aerosol optical properties in urban areas from very high spatial resolution images

Applied Optics ◽

10.1364/ao.50.005408 ◽

2011 ◽

Vol 50 (28) ◽

pp. 5408 ◽

Cited By ~ 2

Author(s):

Colin Thomas ◽

Xavier Briottet ◽

Richard Santer

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

Spatial Resolution ◽

Urban Areas ◽

High Spatial Resolution ◽

Aerosol Optical Properties ◽

Very High Spatial Resolution ◽

Very High

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Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data

International Journal of Remote Sensing ◽

10.1080/01431160701311259 ◽

2007 ◽

Vol 29 (4) ◽

pp. 1153-1167 ◽

Cited By ~ 32

Author(s):

C. Delenne ◽

S. Durrieu ◽

G. Rabatel ◽

M. Deshayes ◽

J. S. Bailly ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Sensing Data ◽

Very High Spatial Resolution ◽

Very High

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Towards a green sustainable strategy for Mediterranean cities: Assessing the benefits of large-scale green roofs implementation in Thessaloniki, Northern Greece, using environmental modelling, GIS and very high spatial resolution remote sensing data

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2015.11.098 ◽

2016 ◽

Vol 58 ◽

pp. 510-525 ◽

Cited By ~ 46

Author(s):

Marinos Karteris ◽

Ifigeneia Theodoridou ◽

Giorgos Mallinis ◽

Emmanouel Tsiros ◽

Apostolos Karteris

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Large Scale ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Green Roofs ◽

Environmental Modelling ◽

Northern Greece ◽

Very High Spatial Resolution ◽

Very High

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A KERNEL METHOD BASED ON TOPIC MODEL FOR VERY HIGH SPATIAL RESOLUTION (VHSR) REMOTE SENSING IMAGE CLASSIFICATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-399-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 399-403

Author(s):

Linmei Wu ◽

Li Shen ◽

Zhipeng Li

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Topic Model ◽

High Spatial Resolution ◽

Kernel Method ◽

Remote Sensing Image ◽

Structure Information ◽

Remote Sensing Image Classification ◽

Very High Spatial Resolution ◽

Very High

A kernel-based method for very high spatial resolution remote sensing image classification is proposed in this article. The new kernel method is based on spectral-spatial information and structure information as well, which is acquired from topic model, Latent Dirichlet Allocation model. The final kernel function is defined as K = u1Kspec + u2Kspat + u3Kstru, in which Kspec, Kspat, Kstru are radial basis function (RBF) and u1 + u2 + u3 = 1. In the experiment, comparison with three other kernel methods, including the spectral-based, the spectral- and spatial-based and the spectral- and structure-based method, is provided for a panchromatic QuickBird image of a suburban area with a size of 900 × 900 pixels and spatial resolution of 0.6 m. The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based which accuracy respectively is 67 % and 74 %. What's more, the accuracy of the proposed composite kernel method that jointly uses the spectral, spatial, and structure information is highest among the four methods which is increased to 83 %. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.

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