ClouDet: A Dilated Separable CNN-Based Cloud Detection Framework for Remote Sensing Imagery

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Hongwei Guo Hongyang Bai Weiwei Qin
Abstract	Cloud detection is one of the essential procedures in optical remote sensing image processing because clouds are widely distributed in remote sensing images and cause a lot of challenges, such as climate research and object detection. In this article, a lightweight deep-learning-based framework is proposed to detect cloud in remote sensing imagery. First, a multiple features fusion strategy is designed to extract learnable manual features and convolution features from visible and near-infrared bands. Then, a lightweight fully convolutional neural network (ClouDet) with a microarchitecture named dilated separable convolutional module is used to extract the multiscale contextual information and gradually recovers segmentation results with the same size as input image, which is more effective for large-scale cloud detection with larger receptive field, less parameters, and lower compute complexity. Third, context pooling is designed to amend the possible misjudgments. Visual and quantitative comparison experiments are conducted on several public cloud detection datasets, which indicates that our proposed method can accurately detect clouds under different conditions, which is more effective and accurate than the compared state-of-the-art methods.
Related Links	https://ieeexplore.ieee.org/document/9543563/
e-ISSN	21511535
DOI	10.1109/JSTARS.2021.3114171
Journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume Number	14
Language	English
Publisher	IEEE
Publisher Date	2021-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Ocean Engineering Geophysics. Cosmic Physics Cloud Detection Cloud Segmentation Convolutional Neural Network (cnn) Deep Learning Remote Sensing
Content Type	Text
Resource Type	Article