TY  - EJOU
AU  - Salleh, Siti Aekbal 
AU  - Khalid, Nafisah 
AU  - Danny, Natasha 
AU  - Zaki, Nurul Ain Mohd. 
AU  - Ustuner, Mustafa 
AU  - Latif, Zulkiflee Abd 
AU  - Foronda, Vladimir 

TI  - Support Vector Machine (SVM) and Object Based Classification in Earth Linear Features Extraction: A Comparison
T2  - Revue Internationale de Géomatique

PY  - 2024
VL  - 33
IS  - 1
SN  - 2116-7060

AB  - Due to the spectral and spatial properties of pervious and impervious surfaces, image classification and information extraction in detailed, small-scale mapping of urban surface materials is quite difficult and complex. Emerging methods and innovations in image classification have centred on object-based classification techniques and various segmentation techniques, which are fundamental to this approach. Consequently, the purpose of this study is to determine which classification method is most suitable for extracting linear features in terms of techniques and performance by comparing two classification methods, pixel-based approach and object-based approach, using WorldView-2 satellite imagery to specifically highlight linear features such as roads, building edges, and road dividers. Two applied algorithms, including support vector machines (SVM) and ruled-based, were evaluated using two distinct software. A comparison of the results reveals that the object-based classification has a higher overall resolution than the pixel-based classification. The output of rule-based classification was satisfactory, with an overall accuracy of 88.6% (ENVI) and 92.2% (e-Cognition). The SVM classification result contained misclassified impervious surfaces and other urban features, as well as mixed objects. This classification achieved an overall accuracy of 75.1%. Nonetheless, this study provides an excellent overview for understanding the differences in their performances on the same data, as well as a comparison of the software employed.
KW  - Support vector machine (SVM); remote sensing; image classification

DO  - 10.32604/rig.2024.050723