Recurrent neural networks for remote sensing image classification


Creative Commons License

Lakhal M. I., ÇEVİKALP H., Escalera S., Ofli F.

IET COMPUTER VISION, vol.12, no.7, pp.1040-1045, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 7
  • Publication Date: 2018
  • Doi Number: 10.1049/iet-cvi.2017.0420
  • Journal Name: IET COMPUTER VISION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1040-1045
  • Keywords: remote sensing, geophysical image processing, recurrent neural nets, image classification, feature extraction, learning (artificial intelligence), computer vision, recurrent neural networks, remote sensing image classification, automatic image classification, computer vision, deep learning, deep recurrent architecture, high-level feature descriptors, general encoder-decoder framework, recurrent network structure, UC Merced dataset, RS-19 dataset, Brazilian Coffee Scenes dataset
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder-decoder framework. To the best of the authors' knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset.