Cited By
View all- Nisa U(2024)Image augmentation approaches for small and tiny object detection in aerial images: a reviewMultimedia Tools and Applications10.1007/s11042-024-19768-7Online publication date: 1-Aug-2024
An Intelligent Registration Method of Heterogeneous Remote Sensing Images Based on Style Transfer
Pages 637 - 642
Abstract
Intelligent registration of heterogeneous remote sensing images is a hot issue in the field of remote sensing and has important research and application values. Due to the significant differences in data sources, image texture, color space and other factors of heterogeneous remote sensing images, the traditional image registration algorithms are difficult to be directly applied to the intelligent registration of images from different sources. In this paper, a new method of intelligent registration of heterogenous remote sensing images based on style transfer is proposed. First, the content features of the baseline image and the style features of the image to be aligned are extracted by using the generative adversarial nets, and the remote sensing images from different sources are fused in terms of content and style to obtain the remote sensing image transfer results with the same style as the image to be aligned and keeping the content of the baseline image unchanged. Then, an intelligent image registration algorithm is used to match the migrated remote sensing images. Finally, a heterogenous remote sensing image dataset is constructed around the heterogenous remote sensing image registration task, and the intelligent registration method of this paper is experimentally validated based on this dataset. The experimental results show that the intelligent image registration method based on style transfer can effectively improve the accuracy of heterogenous remote sensing image registration compared with the direct image registration method.
References
[1]
Lowe D G . Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110. Atul Adya, Paramvir Bahl, Jitendra Padhye,
[2]
Bay H, Tuytelaars T, Gool L V. Surf: Speeded up robust features[C]. European conference on computer vision. Springer, Berlin, Heidelberg, 2006: 404-417.
[3]
Du B, Ru L, Wu C, Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 9976-9992.
[4]
Jianqiang L, Wangzhi L, Yubin L. Agricultural aerial remote sensing image registration algorithm based on point feature detection [J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(3): 71-77.
[5]
Dusmanu M, Rocco I, Pajdla T, D2-net: A trainable cnn for joint detection and description of local features[J]. arXiv preprint arXiv:1905.03561, 2019.
[6]
Taunk K, De S, Verma S, A brief review of nearest neighbor algorithm for learning and classification[C]//2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019: 1255-1260.
[7]
Tan X, Yang J, Deng X. Filtering method of star control points for geometric correction of remote sensing image based on RANSAC algorithm[C]//Ninth International Conference on Graphic and Image Processing (ICGIP 2017). SPIE, 2018, 10615: 1216-1221.
[8]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J].Computer Science. 2014, 229(36):1409-1556.
[9]
Leng C, Zhang H, Li B, Local feature descriptor for image matching: A survey[J]. IEEE Access, 2018, 7: 6424-6434.
[10]
He K, Sun J. Convolutional neural networks at constrained time cost[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 5353-5360.
[11]
Dusmanu M, Rocco I, Pajdla T, D2-net: A trainable cnn for joint description and detection of local features[C]//Proceedings of the ieee/cvf conference on computer vision and pattern recognition. 2019: 8092-8101.
[12]
Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823.
Index Terms
- An Intelligent Registration Method of Heterogeneous Remote Sensing Images Based on Style Transfer
Recommendations
Remote sensing image registration techniques: a survey
ICISP'10: Proceedings of the 4th international conference on Image and signal processingImage Registration is the first step towards using remote sensed images for any purpose. Despite numerous techniques being developed for image registration, only a handful has proved to be useful for registration of remote sensing images due to their ...
Fully automatic and robust approach for remote sensing image registration
CIARP'07: Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applicationsImage registration is an important preprocessing procedure for remote sensing image applications, such as geometric correction, change detection, and image fusion. Since it is a time-consuming and labor-intensive task to correctly register the remote ...
Wavelet-based image registration technique for high-resolution remote sensing images
Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints at different times or by different sensors. It is an important image processing procedure in remote sensing and has ...
Comments
Information & Contributors
Information
Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 35Total Downloads
- Downloads (Last 12 months)19
- Downloads (Last 6 weeks)2
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
Cited By
View all- Nisa U(2024)Image augmentation approaches for small and tiny object detection in aerial images: a reviewMultimedia Tools and Applications10.1007/s11042-024-19768-7Online publication date: 1-Aug-2024
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format