A Novel Land Use Classifier with Convolutional Recurrent Structure

Through the development of machine learning and computer vision, image scene classification has made immense progress over the last decade. Remote sensing land use analysis remains a topic of great interest. Using deep learning methods from computer vision, we develop a novel approach that combines a convolutional structure and gated recurrent unit layers with a fully connected neural network to solve land use classification tasks. Simulation studies confirm the proposed method can more accurately classify and recognize remote sensing images in the EuroSAT dataset than current state-of-the-art algorithms.