A new object-class based gap-filling method for PlanetScope satellite image time series

• Published in: Remote sensing of environment Vol. 280; p. 113136
• Main Authors: Wang, Jing, Lee, Calvin K.F., Zhu, Xiaolin, Cao, Ruyin, Gu, Yating, Wu, Shengbiao, Wu, Jin
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2022
• Subjects: Cloud removal; cropland; CubeSats; environment; forests; Gap-filling; Image reconstruction; land cover; landscapes; Object-based segmentation; remote sensing; time series analysis; Cloud removal; Object-based segmentation; Gap-filling; CubeSats; Image reconstruction
• ISSN: 0034-4257; 1879-0704; 1879-0704
• DOI: 10.1016/j.rse.2022.113136

PlanetScope CubeSats data with a 3-m resolution, frequent revisits, and global coverage have provided an unprecedented opportunity to advance land surface monitoring over the recent years. Similar to other optical satellites, cloud-induced data missing in PlanetScope satellites substantially hinders its use for broad applications. However, effective gap-filling in PlanetScope image time series remains challenging and is subject to whether it can 1) consistently generate high accuracy results regardless of different gap sizes, especially for heterogeneous landscapes, and 2) effectively recover the missing pixels associated with rapid land cover changes. To address these challenges, we proposed an object-class based gap-filling (‘OCBGF’) method. Two major novelties of OCBGF include 1) adopting an object-based segmentation method in conjunction with an unsupervised classification method to help characterize the landscape heterogeneity and facilitate the search of neighboring valid pixels for gap-filling, improving its applicability regardless of the gap size; 2) employing a scenario-specific gap-filling approach that enables effective gap-filling of areas with rapid land cover change. We tested OCBGF at four sites representative of different land cover types (plantation, cropland, urban, and forest). For each site, we evaluated the performance of OCBGF on both simulated and real cloud-contaminated scenarios, and compared our results with three state-of-the-art methods, namely Neighborhood Similar Pixel Interpolator (NSPI), AutoRegression to Remove Clouds (ARRC), and Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS). Our results show that across all four sites, OCBGF consistently obtains the highest accuracy in gap-filling when applied to scenarios with various gap sizes (RMSE = 0.0065, 0.0090, 0.0092, and 0.0113 for OCBGF, SAMSTS, ARRC, and NSPI, respectively) and with/without rapid land cover changes (RMSE = 0.0082, 0.0112, 0.0119, and 0.0120 for OCBGF, SAMSTS, ARRC, and NSPI, respectively). These results demonstrate the effectiveness of OCBGF for gap-filling PlanetScope image time series, with potential to be extended to other satellites. •An automatic gap-filling method was developed for PlanetScope satellites.•The method was rigorously evaluated across four sites with diverse land cover types.•The method was compared with the other three state-of-the-art methods.•Our method has the highest accuracy and could help recover the fine-scale phenology variability.