scholarly journals Applying object-based image analysis and knowledge-based classification to ADS-40 digital aerial photographs to facilitate complex forest land cover classification

2017 ◽  
Vol 11 (1) ◽  
pp. 015001 ◽  
Author(s):  
Yi-Ta Hsieh ◽  
Chaur-Tzuhn Chen ◽  
Jan-Chang Chen
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Classification ◽  
Aerial Photographs ◽  
Forest Land ◽  
Object Based Image Analysis ◽  
Knowledge Based ◽  
Object Based ◽  
Forest Land Cover

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.

scholarly journals Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas

2020 ◽  
Vol 12 (11) ◽  
pp. 1721 ◽  
Author(s):  
Fernanda F. Ribeiro ◽  
Dar A. Roberts ◽  
Laura L. Hess ◽  
Frank W. Davis ◽  
Kelly K. Caylor ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Forest Land ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Fine Spatial Resolution ◽  
Neotropical Savannas ◽  
Geographic Object ◽  
Forest Land Cover ◽  
Map Accuracy

Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (<10 m) can improve map accuracy in these heterogeneous landscapes. Traditional pixel-based classification methods have proven problematic for fine spatial resolution data due to increased within-class spectral variability. Geographic Object-Based Image Analysis (GEOBIA) is a robust alternative method to overcome these issues. We developed a systematic GEOBIA framework accounting for both spectral and spatial features to map Cerrado structural types at 5-m resolution. This two-step framework begins with image segmentation and a Random Forest land cover classification based on spectral information, followed by spatial contextual and topological rules developed in a systematic manner in a GEOBIA knowledge-based approach. Spatial rules were defined a priori based on descriptions of environmental characteristics of 11 different physiognomic types and their relationships to edaphic conditions represented by stream networks (hydrography), topography, and substrate. The Random Forest land cover classification resulted in 10 land cover classes with 84.4% overall map accuracy and was able to map 7 of the 11 vegetation classes. The second step resulted in mapping 13 classes with 87.6% overall accuracy, of which all 11 vegetation classes were identified. Our results demonstrate that 5-m spatial resolution imagery is adequate for mapping land cover types of savanna structural elements. The GEOBIA framework, however, is essential for refining land cover categories to ecological classes (physiognomic types), leading to a higher number of vegetation classes while improving overall accuracy.

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

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
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