International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W9
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W9, 229–235, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W9-229-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W9, 229–235, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W9-229-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Oct 2018

30 Oct 2018

MAPPING OF HIGHLY HETEROGENEOUS URBAN STRUCTURE TYPE FOR FLOOD VULNERABILITY ASSESSMENT

T. H. Tam1, M. Z. Abd Rahman1, S. Harun2, and I. U. Kaoje1 T. H. Tam et al.
  • 1TropicalMap Research Group, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
  • 2Dept. of Hydraulic and Hydrology, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

Keywords: Urban Structure Type, Object-based image analysis, Random forest, Support vector machine, CART, Flood vulnerability

Abstract. Vulnerability plays an important role in risk assessment. For flood vulnerability assessment, the map and characteristics of elements-at-risk at different scales are strongly required depending on the risk and vulnerability assessment requirements. This study proposes a methodology to classify urban structure type by combining object-based image classification and different high resolution remote sensing data. In this study, a high resolution satellite image and LiDAR have been acquired over Kota Bharu, Kelantan which consists of highly heterogeneous urban structure type (UST) classes. The first stage is data pre-processing that includes orthorectification and pansharpening of Geoeye satellite image, image resampling for normalised Digital Surface Model (nDSM) and followed by image segmentation for creating meaningful objects. The second stage comprises of derivation of image features, generation of training and testing datasets, and classification of UST. The classification was based on three types of machine learning classifiers, i.e. Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Tree (CART). The results obtained from the classification processes were compared using individual omission and commission error, overcall accuracy and Kappa coefficient. The results show that Random Forest classifier with all image features achieved the highest overall accuracy (93.5%) and Kappa coefficient (0.94). This is followed by CART classifier with overall accuracy of 93.7% and Kappa coefficient of 0.92. Finally, SVM classifier produced the lowest overall accuracy and Kappa coefficient with 88.6% and 0.86, respectively. The UST classification result can be further used to assist detailed building characterisation for large scale flood vulnerability assessment.