Remote Sensing Image Classification Based on Convolutional Neural Networks

Abstract

Nowadays, large amounts of high resolution remote-sensing images are acquired daily. However, the satellite image classification is requested for many applications such as modern city planning, agriculture and environmental monitoring. Many researchers introduce and discuss this domain but still, the sufficient and optimum degree has not been reached yet. Hence, this article focuses on evaluating the available and public remote-sensing datasets and common different techniques used for satellite image classification. In recent years, there has been an extensive popularity of supervised deep learning methods in various remote-sensing applications, such as geospatial object detection and land use scene classification. Thus, the experiments, in this article, are carried out based on HSV Color Space using one of the popular deep learning models, Convolution Neural Networks (CNNs), precisely, AlexNet architecture with SVM classifier on 7 different standard datasets. It has reached about 99.7 ± 0.02% of HSV color space for the high resolution dataset, PatternNet. Finally, a comparison with other different techniques is highlighted.