Transfer Learning for Changes Detection in Optical Remote Sensing Imagery

Abstract

Change detection in land and urban environments has been an important task in remote sensing field. Deep learning has recently received an increasing attention from researchers and has been successfully applied for many domains. In remote sensing, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and the costly annotation, which limits its development. In this work, we introduce deep transfer learning as a way of overcoming the application of deep learning techniques for optical remote sensing change detection. Through several experiments, we investigate various uses of pre-trained Convolutional Neural Networks (CNNs) models. Our experiments on bi-temporal dataset show that VGG16 and ResNet networks consistently yield the best performances across considered strategies. It also appears that fine-tuning pre-trained CNN models is the best performing strategies.