Remote sensing image cloud detection algorithm based on residual network module and pyramid structure of dilated convolution

Yi Chen; Ying Zhu

doi:10.1117/12.3005171

7 March 2024 Remote sensing image cloud detection algorithm based on residual network module and pyramid structure of dilated convolution

Yi Chen, Ying Zhu

Author Affiliations +

Proceedings Volume 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1308809 (2024) https://doi.org/10.1117/12.3005171
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

Approximately 50% of the Earth’s surface is covered by clouds. Optical remote sensing satellites encounter challenges in capturing clear ground images due to the difficulty of visible photoelectric magnetic waves penetrating clouds. Therefore, cloud detection is an essential and basic step in the processing and application of optical satellite remote sensing images. The traditional threshold-based cloud detection methods utilize spectral information of images to set thresholds, which is sensitive and less adaptable to varying imaging conditions. To enhance cloud detection performance, this paper proposes a deep learning method that combines residual network module and pyramid structure of dilated convolution. The method employs an encoder-decoder structure where the residual convolution module replaces conventional convolution kernels, reducing parameter quantity while enhancing feature expression capabilities. Furthermore, a pyramid structure of dilated convolution is introduced between the encoder and decoder to improve the acquisition of global information and reduce misjudgment of cloud pixels. In this work, ablation experiments are conducted to validate the reliability of the proposed network model. To evaluate the effectiveness of the proposed method, GaoFen-1 remote sensing image data was used for experimental verification. The results indicated that compared to traditional methods, the proposed approach achieves satisfactory cloud detection results in various surface types, including barren land, forests, grasslands/crops, and wetlands, while having a smaller model size and parameter quantity.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi Chen and Ying Zhu "Remote sensing image cloud detection algorithm based on residual network module and pyramid structure of dilated convolution", Proc. SPIE 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1308809 (7 March 2024); https://doi.org/10.1117/12.3005171

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Clouds

Convolution

Remote sensing

Detection and tracking algorithms

Satellites

Deep learning

Image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years