Accuracy Evaluation of Convolutional Neural Network Classification Algorithms for Building Identification in Rural and Urban Areas from Very High-Resolution Satellite Imagery in Jambi, Indonesia

An accurate land cover data is essential for a reliable decision-making process; therefore, researchers have turned to novel land cover classification algorithms employing machine learning on high-resolution satellite imagery to improve classification accuracy. One of the emerging machine learning algorithms for land cover classification is the convolutional neural network. The purpose of the experiment presented in this paper is to assess the accuracy performance of three patch-based, convolutional neural network architectures: LeNet, VGGNet, and XCeption. The experiment concluded that the convolutional neural network classification algorithms consistently outperformed pixel-based classification algorithms in building high-resolution satellite imagery in the Jambi City area. It was also evident that larger image patch size does not always improve classification accuracy in all CNN architecture evaluated in this study. The experiment showed that the XCeption architecture performed best among the three CNN architectures assessed, with a 72-pixel patch size having the best accuracy.