Deep network based on up and down blocks using wavelet transform and successive multi-scale spatial attention for cloud detection
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep network based on up and down blocks using wavelet transform and successive multi-scale spatial attention for cloud detection
Authors
Keywords
Cloud detection, GaoFen-1, Wavelet, Up block, Down block, Dark channel prior, Spatial attention
Journal
REMOTE SENSING OF ENVIRONMENT
Volume 261, Issue -, Pages 112483
Publisher
Elsevier BV
Online
2021-05-15
DOI
10.1016/j.rse.2021.112483
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- An Effective Cloud Detection Method for Gaofen-5 Images via Deep Learning
- (2020) Junchuan Yu et al. Remote Sensing
- A Cloud Detection Method Using Convolutional Neural Network Based on Gabor Transform and Attention Mechanism with Dark Channel Subnet for Remote Sensing Image
- (2020) Jing Zhang et al. Remote Sensing
- Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning
- (2020) Yansheng Li et al. REMOTE SENSING OF ENVIRONMENT
- Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
- (2019) Zhiwei Li et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A cloud detection algorithm for satellite imagery based on deep learning
- (2019) Jacob Høxbroe Jeppesen et al. REMOTE SENSING OF ENVIRONMENT
- A Cloud Detection Method for Landsat 8 Images Based on PCANet
- (2018) Yue Zi et al. Remote Sensing
- Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning
- (2017) Fengying Xie et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- (2017) Vijay Badrinarayanan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Fully Convolutional Networks for Semantic Segmentation
- (2017) Evan Shelhamer et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery
- (2017) Zhiwei Li et al. REMOTE SENSING OF ENVIRONMENT
- An Iterative Haze Optimized Transformation for Automatic Cloud/Haze Detection of Landsat Imagery
- (2016) Shuli Chen et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Automatic Recognition of Cloud Images by Using Visual Saliency Features
- (2015) Xiangyun Hu et al. IEEE Geoscience and Remote Sensing Letters
- Bag-of-Words and Object-Based Classification for Cloud Extraction From Satellite Imagery
- (2015) Yi Yuan et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and spatial considerations
- (2015) Jian Li et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images
- (2015) Zhe Zhu et al. REMOTE SENSING OF ENVIRONMENT
- Comparative Analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD Sensor Data for Grassland Monitoring Applications
- (2015) Lei Wang et al. Remote Sensing
- Single Image Haze Removal Using Dark Channel Prior
- (2010) Kaiming He et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Spatial and Temporal Varying Thresholds for Cloud Detection in GOES Imagery
- (2008) G.J. Jedlovec et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started