Land use classification based on object and pixel using Landsat 8 OLI in Kendari City, Southeast Sulawesi Province, Indonesia

5th International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring 2018

生态环境科学

Ramadhan Kete, Surya Cipta^1^2 ; Suprihatin^3 ; Tarigan, Suria Darma^4 ; Effendi, Hefni^5

Doctoral Program in Natural Resources and Environmental Management, Bogor Agricultural University, Bogor, Indonesia^1

Department of Environmental Science, Faculty of Forestry and Environmental Science, Halu Oleo University, Kendari, Indonesia^2

Department of Agricultural Industry Technology, Bogor Agricultural University, Bogor, Indonesia^3

Department of Soil and Natural Resource Management, Bogor Agricultural University, Bogor, Indonesia^4

Environmental Research Center, Bogor Agricultural University, Bogor, Indonesia^5

关键词: Classification technique;    Landuse classifications;    Method and technique;    Operational land imager;    Pixel based approach;    Pixel based classifications;    Region of interest;    Support vector machine algorithm;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/284/1/012019/pdf
DOI  :  10.1088/1755-1315/284/1/012019

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Current development on method and technique for data processing in remote sensing has received a great importance, since it provides a base for a myriad of applications, including land use monitoring. OBIA- and pixel-based approach are commonly used as classification technique in remote sensing. This present work aimed to compare both approaches in classifying the land use in Kendari, Southeast Sulawesi Province, using Landsat 8 OLI (Operational Land Imager). Digital images were processed using both techniques with the use of support vector machine (SVM) algorithm. The 188 sampling spots in the study site were randomly determined, then classified into 5 groups of land use: water body, follow land, built up land, residential area, and vegetation. Data obtained were then validated according to data from google earth and ground check. The accuracy was assessed by confusion matrix method using Region of Interest (ROI). The results showed that OBIA-based classification coupled with SVM algorithm showed overall accuracy of 81.38%, Kappa coefficient of 78.77%, in which the accuracy was 9.57% higher than pixel-based classification. Based on this finding, OBIA was reported to produce a better performance in identifying land use in urban area.