Evaluation of Different Approaches of Convolutional Neural Networks for Land Use and Land Cover Classification Based on High Resolution Remote Sensing Images

Abstract

Land use and land cover mapping is very important in the fields of urban planning, land management, and natural resource conservation. Recently, convolutional neural networks (CNNs) are applied widely in land use and land cover (LULC) classification as the acquisition of high resolution satellite images becomes easier owing to technological advancements. In this paper, we explore how to better exploit existed CNNs in LULC classification task. Three different learning modalities: full-trained, fine-tuning and pre-trained CNNs were used as feature extractors, and two promising CNNs models (AlexNet and GoogLeNet) and two remote sensing datasets (UC Merced Land Use dataset and Brazilian Coffee Scenes dataset) were studied. Results show that the both AlexNet and GoogLeNet can be used in high remote sensing classification with a great performance. What is more, the full-trained CNNs is not always the best approach, on the contrary, fine-tuning based on pre-trained CNNs, tends to be the best approach.