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

作　　者：Caihong Ma Fu Chen Jin Yang Jianbo Liu Wei Xia Xinpeng Li 

机构地区：[1]Sanya Institute of Remote Sensing,Sanya,Hainan Province,China [2]Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing,China

出　　处：《Big Earth Data》2018年第4期351-367,共17页地球大数据（英文）

基　　金：This work was supported by the National Natural Science Funds of China[41501116].

摘　　要：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 remotesensing 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.

关 键 词：Content-based remotesensing image retrieval neural network multi-features 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]