scholarly journals Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

2017 ◽  
Vol 9 (9) ◽  
pp. 939 ◽  
Author(s):  
Yaohuan Huang ◽  
Chuanpeng Zhao ◽  
Haijun Yang ◽  
Xiaoyang Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Image Analysis ◽  
Feature Selection ◽  
Land Cover ◽  
Sample Size ◽  
Land Cover Classification ◽  
High Dimensionality ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Geographic Object Based Image Analysis of WorldView-3 Imagery for Urban Hydrologic Modelling at the Catchment Scale

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1133 ◽  
Author(s):  
Mark Randall ◽  
Rasmus Fensholt ◽  
Yongyong Zhang ◽  
Marina Bergen Jensen
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Classification ◽  
Supplementary Data ◽  
Catchment Scale ◽  
Hydrologic Modelling ◽  
Sponge City ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

China’s Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation.

Object-based image analysis for urban land cover classification in the city of Campinas - SP, Brazil

2015 ◽  
Author(s):  
David G. M. Franca ◽  
Rodolfo G. Lotte ◽  
Claudia M. de Almeida ◽  
Sacha M. O. Siani ◽  
Thales S. Korting ◽  
...  
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Object Based Image Analysis ◽  
Urban Land Cover ◽  
Object Based ◽  
The City

Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis

2012 ◽  
Vol 33 (19) ◽  
pp. 5973-5995 ◽  
Author(s):  
Carolina Moutinho Duque de Pinho ◽  
Leila Maria Garcia Fonseca ◽  
Thales Sehn Korting ◽  
Cláudia Maria de Almeida ◽  
Hermann Johann Heinrich Kux
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Land Cover ◽  
Urban Environment ◽  
Land Cover Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
High Resolution Images

Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

2012 ◽  
Vol 33 (14) ◽  
pp. 4603-4633 ◽  
Author(s):  
T. Tormos ◽  
P. Kosuth ◽  
S. Durrieu ◽  
S. Dupuy ◽  
B. Villeneuve ◽  
...  
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Fine Scale ◽  
Regional Mapping ◽  
Multispectral Imagery ◽  
Object Based Image Analysis ◽  
Object Based ◽  
River Corridors ◽  
Thematic Data
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