Using Radial Basis Function Kernel Method
Authors: Nur Suhaili Mansor ; Hapini Awang ; Sarkin Tudu Shehu Malami ; Amirulikhsan Zolkafli ; Mohammed Ahmed Taiye ; Hanhan Maulana
DOI: 10.1007/978-981-99-9589-9_23
Abstract
Machine learning, particularly Support Vector Machines (SVM), has gained popularity in geospatial data processing and image classification. Geospatial data from various sources may contain errors, impacting image classification accuracy. Traditional pixel-based and object-based methods struggle to classify complex land cover classes accurately. Previous studies explored machine learning algorithms like Random Forests, K-Nearest Neighbors, and Neural Networks. Still, they faced challenges capturing intricate relationships within images and required substantial labeled training data, leading to computational expenses. SVM with polynomial kernels was attempted in some studies, but it suffered from potential overfitting and inefficiency for large datasets. To overcome these issues, this study employed SVM with RBF and Linear kernels to classify multispectral satellite images from the SPOT-6 Satellite Imagery dataset in Sungai Kelang, Malaysia. Previous research evaluated each kernel’s performance accuracy compared using a test dataset, utilizing open-source tools like Jupyter Notebooks and Python libraries to explore SVM’s potential as a high-performance satellite image classification technique. The findings revealed that SVM with RBF kernel outperformed SVM with polynomial or linear kernels in classifying satellite images. The RBF kernel’s robustness allowed SVM to model intricate decision boundaries and capture complex patterns in the image data, making it suitable for tasks with non-linearly separable data. The study introduces a new methodology and theoretical contribution to image classification-related literature, shedding light on the efficacy of SVM-RBF for geospatial data processing. It provides an alternative to traditional approaches for complex image classification tasks. Moreover, the research assists in selecting the optimal algorithm for remote sensing and satellite imagery applications. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author keywords
Geospatial; Image Classification; Linear Kernel; Radial basis function (RBF) Kernel; Support Vector Machine
Indexed keywords
Engineering controlled terms
Classification (of information); Complex networks; Data handling; Forestry; Large datasets; Learning algorithms; Nearest neighbor search; Radial basis function networks; Remote sensing; Satellite imagery; Statistical tests; Support vector machines
Engineering uncontrolled terms
Geo-spatial; Geospatial data processing; Images classification; Kernel-methods; Linear kernel; Performance; Polynomial kernels; Radial basis function kernels; Satellite image classification; Support vectors machine
Engineering main heading
Image classification
This article can be accessed at https://www.scopus.com/pages/publications/85185723250









