Abstract
ABSTRACTWith 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.
Highlights
With the development of remote-sensing technology, the volume of image data that is received from satellites has become huge (Liu, Yang, Chen, Dai, & Zhang, 2014; Yasar, Hatipoglu, & Ceylan, 2015)
Statistical results The results obtained for different feature categories using the remote-sensing image-retrieval model based-on the ensemble neural networks (ENNs) were compared with those obtained using 12 other neural networks
Search examples To illustrate the effectiveness of our approach for the querying of remote-sensing images using a ENN, we provide here some screenshots obtained from our Content-based remotesensing image retrieval (CBRSIR) system
Summary
With the development of remote-sensing technology, the volume of image data that is received from satellites has become huge (Liu, Yang, Chen, Dai, & Zhang, 2014; Yasar, Hatipoglu, & Ceylan, 2015). The widespread availability of high spatial resolution remotesensing images is producing an explosion in the volume of acquired data but the amount of detail in the imagery is increasing by orders of magnitude (Datcu et al 2005; Wang, Shao, Zhou, & Liu, 2014). This is collectively called big Earth data (Guo, 2017). State-of-the-art systems for accessing remote-sensing images often rely on keywords or tags that relate to geographical coordinates, the data acquisition time or the sensor type (Ma, Dai, Liu, Liu, & Yang, 2014).
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