A Novel Land Use Classifier with Convolutional Recurrent Structure
- Resource Type
- Conference
- Authors
- Xie, Dong; Depoian, Arthur C.; Bailey, Colleen P.
- Source
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Geoscience and Remote Sensing Symposium IGARSS , 2021 IEEE International. :6268-6271 Jul, 2021
- Subject
- Aerospace
Geoscience
Photonics and Electrooptics
Signal Processing and Analysis
Deep learning
Computer vision
Neural networks
Logic gates
Robustness
Classification algorithms
Sensors
computer vision
remote sensing
deep learning
neural networks
land use classification
- Language
- ISSN
- 2153-7003
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.