Open AccessRecurrent neural networks for remote sensing image classification
Author(s) -
Lakhal Mohamed Ilyes,
Çevikalp Hakan,
Escalera Sergio,
Ofli Ferda
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0420
Subject(s) - computer science , artificial intelligence , feature (linguistics) , task (project management) , encoder , deep learning , feature extraction , image (mathematics) , machine learning , contextual image classification , recurrent neural network , artificial neural network , pattern recognition (psychology) , architecture , feature learning , art , philosophy , linguistics , visual arts , management , economics , operating system
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.