Slums Detection on Worldview-3 Imagery Based-on Integration of Image Sharpening and Lacunarity Algorithm

Population growth and the increasing movement of people in the city will grow new residential area. Ease of access to employment and public facilities location could trigger illegal occupancy in certain areas. Remote sensing data is able to present spatial information in great detail so it can be used as a data source in evaluating the condition of urban settlements. The purpose of this study were to detect the slums resulting from lacunarity algorithm, and determine the accuracy of the resulting image WorldView-3 in the detection and identification of slums with lacunarity algorithm. The method used was remote sensing method with qualitative and quantitative analysis of the lacunarity algorithm and slum variables from Ministry of Public Works (PU). The image Sharpening Worldview 3 (recorded July 27,2015) was used to interpretation of the utilization of settlement blocs. A field survey on April 5 to May 10, 2016 was conducted to examine the results of the classification and utilization of settlement blocs, slums from algorithms lacunarity and slums from PU. The results showed that the image of WorldView-3 provides moderate accuracy in the detection of slums (33.3% on a moving window 7x7, 50.0% on a moving window 5x5, and 58.3% on a moving window 3x3). The smaller size of the moving window that is used will increase the ability of the algorithm to detect slums