|Table of Contents|

Hi-Res Remote Image Searching Based on Ensemble Contrastive Learning(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2025年01期
Page:
22-29
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Hi-Res Remote Image Searching Based on Ensemble Contrastive Learning
Author(s):
Gu Chengcheng1; Sun Yong1; 2; Cheng Qianxi3; Tan Wenan1
(1.School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China)
(2.Key Laboratory of Physical Geographic Environment of Anhui Province,Chuzhou University,Chuzhou 239000,China)
(3.School of Resources and Environmental Engineering,Anhui University,Hefei 230009,China)
Keywords:
contrastive learning; ensemble learning; nearest-neighbor contrastive learning; momentum contrastive learning; remote image search
PACS:
TP751; TP18
DOI:
10.3969/j.issn.1672-1292.2025.01.003
Abstract:
To address the issue of currently many single contrastive learning models focusing only on local features of images,a new high resolution remote image searching model based on ensemble contrastive learning is proposed. Firstly,local and global features and learned from remote images by momentum contrastive learning and nearest neighbor contrastive learning methods. Secondly,on this basis,high resolution remote image searching model based on ensemble contrastive learning is proposed,which sets different weights for different contrastive learners according to their own performance in downstream classification tasks,and then further optimizes the learning speeds of learners with the accuracy of contrastive learning features. Finally,ensemble contrastive learning model is applied in Hi-Res remote image searching. Experiments are conducted by using public remote sensing image datasets including EuroSat,UCmerced,WHU-RS19,and PatternNet. The results indicate that the proposed model demonstrates a more stable improvement in accuracy compared with traditional contrastive methods for image searching tasks.

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Last Update: 2025-03-15