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Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Wang, Chisheng, Wang, Shuying, Cui, Hongxing, Sebela, Monja B., Zhang, Ce, Gu, Xiaowei, Fang, Xu, Hu, Zhongwen, Tang, Qiandi, Wang, Yongquan and others. (2021) Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14 . pp. 6923-6936. ISSN 1939-1404. (doi:10.1109/JSTARS.2021.3094586) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90405)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/JSTARS.2021.3094586

Abstract

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications. To recover the cloud obscured information, some preconditions must be satisfied. For example, the cloud must be semitransparent or relationships between contaminated and cloud-free pixels must be assumed. Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Recently, the use of passenger aircraft as a platform for remote sensing has been proposed by some researchers and institutes, including Airbus and the Japan Aerospace Exploration Agency. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multiangle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: first, multiangle image acquisition from passenger aircraft, second, cloud detection based on deep learning semantic segmentation models, third, cloud removal by image stacking, and fourth, image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and predetermination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate.

Item Type: Article
DOI/Identification number: 10.1109/JSTARS.2021.3094586
Uncontrolled keywords: Clouds; Aircraft; Remote sensing; Satellites; Cameras; Optical sensors; Optical imaging; Cloud removal; deep learning; haze removal; multiple viewing angles; passenger aircraft; photogrammetry
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 10:28 UTC
Last Modified: 29 Sep 2021 10:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90405 (The current URI for this page, for reference purposes)
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