The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 249–256, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-249-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 249–256, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-249-2021

  18 Nov 2021

18 Nov 2021

SEMI-AUTOMATED DELINEATION OF INFORMAL SETTLEMENT STRUCTURES FROM DRONE RGB IMAGERY USING OBJECT-BASED IMAGE ANALYSIS

R. A. B. Rivera, E. N. B. Idago, A. C. Blanco, and K. A. P. Vergara R. A. B. Rivera et al.
  • Department of Geodetic Engineering, College of Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines

Keywords: Object-Based Image Analysis, Informal Settlement, Delineation, Drone RGB Imagery

Abstract. With the problem of informal settlements in the Philippines, mapping such areas is the first step towards improvement. Object-based image analysis (OBIA) has been a powerful tool for mapping and feature extraction, especially for high-resolution datasets. In this study, an informal settlement area in UP Diliman, Quezon City was chosen to be the subject site, where individual informal settlement structures (ISS) were delineated and estimated using OBIA. With the help of photogrammetry and image enhancement techniques, derivatives such as elevation model and orthophotos were produced for easier interpretation. An initial rule-set was developed to remove all non-ISS features from the base image–utilizing spectral values and thematic layers as main classifiers. This classification technique yielded a 94% accuracy for non-ISS class, and 92% for the possible ISS class. Another rule-set was then developed to delineate individual ISS based on the texture and elevation model of the area, which paved the way for the estimation of ISS count. To test the robustness of the methodology developed, the estimation results were compared to the manual count obtained through an online survey form, and the classification and delineation results were assessed through overall and individual quality checks. The estimation yielded a relative accuracy of 60%, which came from the delineation rate of 63%. On the other hand, delineation accuracy was calculated through area-based and number-based measures, yielding 58% and 95%, respectively. Issues such as noisy elevation models and physical limitations of the area and survey done affected the accuracy of the results.