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

2011 ◽  
Vol 50 (28) ◽  
pp. 5408 ◽  
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

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.

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

2007 ◽  
Vol 29 (4) ◽  
pp. 1153-1167 ◽  
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

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

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 <i>K</i>&thinsp;=&thinsp;<i>u<sub>1</sub></i><i>K</i><sup>spec</sup>&thinsp;+&thinsp;<i>u<sub>2</sub></i><i>K</i><sup>spat</sup>&thinsp;+&thinsp;<i>u<sub>3</sub></i><i>K</i><sup>stru</sup>, in which <i>K</i><sup>spec</sup>, <i>K</i><sup>spat</sup>, <i>K</i><sup>stru</sup> are radial basis function (RBF) and <i>u<sub>1</sub></i>&thinsp;+&thinsp;<i>u<sub>2</sub></i>&thinsp;+&thinsp;<i>u<sub>3</sub></i>&thinsp;=&thinsp;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&thinsp;×&thinsp;900 pixels and spatial resolution of 0.6&thinsp;m. The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80&thinsp;%, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based which accuracy respectively is 67&thinsp;% and 74&thinsp;%. 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&thinsp;%. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.

Sign in / Sign up

Export Citation Format

Share Document