A Hybrid Model-Based and Data-Driven Approach for Cloud Removal in Satellite Imagery Using Multi-Scale Distortion-Aware Networks
- Resource Type
- Conference
- Authors
- Yu, Weikang; Zhang, Xiaokang; Pun, Man-On; Liu, Ming
- Source
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Geoscience and Remote Sensing Symposium IGARSS , 2021 IEEE International. :7160-7163 Jul, 2021
- Subject
- Aerospace
Geoscience
Photonics and Electrooptics
Signal Processing and Analysis
Reflectivity
Clouds
Atmospheric modeling
Optical distortion
Feature extraction
Distortion
Optical imaging
cloud removal
image restoration
cloud distortion
deep learning
- Language
- ISSN
- 2153-7003
Cloud layer contamination is a common problem in optical remote sensing images. Cloud removal from remote sensing images has attracted increasing attention in recent years. To this end, we propose a multi-scale distortion-aware network for cloud removal from remote sensing images. A novel Cloud Aware and Feature Extraction (CAFE) module is developed by incorporating the physical model of cloud distortion considering atmosphere light, cloud reflectance light and cloud transmission. These distortion factors in CAFE module are encoded into trainabile parameters for feature extraction from contaminated images. The network is trained in an end-to-end manner with cloud contaminated images and ground truth data. Finally, experimentl results on the Remote sensing Image Cloud rEmoving (RICE) dataset demonstrate the effectiveness of the proposed approach.