A remote-sensing image-retrieval model based on an ensemble neural networks
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 user...
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: | |
Online Access: | http://dx.doi.org/10.1080/20964471.2019.1570815 |
Similar Items
-
A Content-Based Remote Sensing Image Change Information Retrieval Model
by: Caihong Ma, et al.
Published: (2017-10-01) -
Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval
by: Xu Tang, et al.
Published: (2018-08-01) -
A Benchmark Dataset for Performance Evaluation of Multi-Label Remote Sensing Image Retrieval
by: Zhenfeng Shao, et al.
Published: (2018-06-01) -
Coarse-to-Fine Deep Metric Learning for Remote Sensing Image Retrieval
by: Min-Sub Yun, et al.
Published: (2020-01-01) -
Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval
by: Dongjie Ye, et al.
Published: (2017-11-01)