A remote-sensing image-retrieval model based on an ensemble neural networks

• Main Authors: Caihong Ma, Fu Chen, Jin Yang, Jianbo Liu, Wei Xia, Xinpeng Li
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-10-01
• Series: Big Earth Data
• Subjects: Content-based remote-sensing image retrieval; neural network; multi-features
• Online Access: http://dx.doi.org/10.1080/20964471.2019.1570815

With the rapid development of remote-sensing technology and the increasing number of Earth observation satellites, the volume of image datasets is growing exponentially. The management of big Earth data is also becoming increasingly complex and difficult, with the result that it can be hard for users to access the imagery that they are interested in quickly, efficiently and intelligently. To address these challenges, this paper proposes a remote-sensing image-retrieval model based on an ensemble neural networks. This model can make full use of existing training data to improve the efficiency and accuracy of the initial retrieval of remote-sensing images and keep model simple. The retrieval of aerial images using the proposed model is compared with the results obtained using ten individual neural networks and two ensemble neural networks and the results show that the proposed approach has a high degree of precision. In addition, the coverage rate and mean precision show a dramatic improvement of more than 40% compared with existing methods based on normal way. And, the coverage ratio gets 86% for the top 10 return results.