Classification of Large-Scale Datasets of Landsat-8 Satellite Image Based on LIBLINEAR Library

Abstract

One of the most common machine learning approaches is linear classification. Multithreading used in LIBLINEAR Library has become an important research topic for processing large datasets. Working in a multithreaded environment accelerates the training of large datasets and increases classification accuracy. This study examines multiple linear support vector machine implementations for binary classification of Landsat-8 satellite image datasets to determine the optimal model. Four datasets have been created for different scenes in Iraq. Each dataset contains millions of pixels to be classified optimally. This article selects an appropriate algorithm for such a large dataset of current types in the LIBLINEAR library optimal for organizing large datasets using the linear SVM algorithm. Each dataset containing about 4.5 million samples was used to test the performance. The seven linear SVM techniques have statically examined the most effective SVM implementation method. Accuracy, F-Score, and Kappa are used as model assessment measures to evaluate and rank the models' performances. Based on the results, the LibLINEAR library with type 4 (LINLINEAR4) classifier was the best classifier for satellite image classification in remote sensing when applied to large datasets. The accuracy, Kappa, and f-score of the LIBLINEAR 4 classifier are as follows: (92.89 %), (73.53 %), and (77.77%) with dataset1, (96.25 %), (59.97 %), and (61.88%) with dataset2, (96.65%), (68.33%), and (70.03%), and (94.72%), (53.67%), and (56.25%), respectively, with dataset4.