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

Chisheng Wang, Shuying Wang, Hongxing Cui, Monja Šebela, Ce Zhang, Xiaowei Gu, Xu Fang, Zhongwen Hu, Qiandi Tang, Yongquan Wang

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)
70 Downloads (Pure)

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.
Original languageEnglish
Article number9477015
Pages (from-to)6923 - 6936
Number of pages14
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume14
Early online date07 Jul 2021
DOIs
Publication statusPublished - 22 Jul 2021

Keywords

  • Cloud removal
  • deep learning
  • haze removal
  • multiple viewing angles
  • passenger aircraft
  • photogrammetry

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