30 November 2017 Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery
Author Affiliations +
Abstract
Land use and land cover (LULC) data are important to monitor and assess environmental change. LULC classification using satellite images is a method widely used on a global and local scale. Especially, urban areas that have various LULC types are important components of the urban landscape and ecosystem. This study aims to classify urban LULC using WorldView-3 (WV-3) very high-spatial resolution satellite imagery and the object-based image analysis method. A decision rules set was applied to classify the WV-3 images in Kathu subdistrict, Phuket province, Thailand. The main steps were as follows: (1) the image was ortho-rectified with ground control points and using the digital elevation model, (2) multiscale image segmentation was applied to divide the image pixel level into image object level, (3) development of the decision ruleset for LULC classification using spectral bands, spectral indices, spatial and contextual information, and (4) accuracy assessment was computed using testing data, which sampled by statistical random sampling. The results show that seven LULC classes (water, vegetation, open space, road, residential, building, and bare soil) were successfully classified with overall classification accuracy of 94.14% and a kappa coefficient of 92.91%.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Sanwit Iabchoon, Sanwit Iabchoon, Sangdao Wongsai, Sangdao Wongsai, Kanoksuk Chankon, Kanoksuk Chankon, } "Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery," Journal of Applied Remote Sensing 11(4), 046015 (30 November 2017). https://doi.org/10.1117/1.JRS.11.046015 . Submission: Received: 21 May 2017; Accepted: 7 November 2017
Received: 21 May 2017; Accepted: 7 November 2017; Published: 30 November 2017
JOURNAL ARTICLE
11 PAGES


SHARE
Back to Top