Recurrent neural networks for remote sensing image classification

Mohamed Ilyes Lakhal, Hakan Çevikalp, Sergio Escalera, Ferda Ofli

Research output: Contribution to journalArticle

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1040-1045
Number of pages6
JournalIET Computer Vision
Issue number7
Publication statusPublished - 1 Oct 2018


ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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