Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90624
PIRA download icon_1.1View/Download Full Text
Title: Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches
Authors: Wei, J
Huang, W
Li, Z
Sun, L
Zhu, X 
Yuan, Q
Liu, L
Cribb, M
Issue Date: Oct-2020
Source: Remote sensing of environment, Oct. 2020, v. 248, 112005
Abstract: A primary challenge in cloud detection is associated with highly mixed scenes that are filled with broken and thin clouds over inhomogeneous land. To tackle this challenge, we developed a new algorithm called the Random-Forest-based cloud mask (RFmask), which can improve the accuracy of cloud identification from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) images. For the development and validation of the algorithm, we first chose the stratified sampling method to pre-select cloudy and clear-sky pixels to form a prior-pixel database according to the land use cover around the world. Next, we select typical spectral channels and calculate spectral indices based on the spectral reflection characteristics of different land cover types using the top-of-atmosphere reflectance and brightness temperature. These are then used as inputs to the RF model for training and establishing a preliminary cloud detection model. Finally, the Super-pixels Extracted via Energy-Driven Sampling (SEEDS) segmentation approach is applied to re-process the preliminary classification results in order to obtain the final cloud detection results. The RFmask detection results are evaluated against the globally distributed United States Geological Survey (USGS) cloud-cover assessment validation products. The average overall accuracy for RFmask cloud detection reaches 93.8% (Kappa coefficient = 0.77) with an omission error of 12.0% and a commission error of 7.4%. The RFmask algorithm is able to identify broken and thin clouds over both dark and bright surfaces. The new model generally outperforms other methods that are compared here, especially over these challenging scenes. The RFmask algorithm is not only accurate but also computationally efficient. It is potentially useful for a variety of applications in using Landsat data, especially for monitoring land cover and land-use changes.
Keywords: Cloud detection
Landsat
Random forest
RFmask
SEEDS
Superpixel segmentation
Publisher: Elsevier
Journal: Remote sensing of environment 
ISSN: 0034-4257
EISSN: 1879-0704
DOI: 10.1016/j.rse.2020.112005
Rights: © 2020 Elsevier Inc. All rights reserved.
© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
The following publication Wei, J., Huang, W., Li, Z., Sun, L., Zhu, X., Yuan, Q., Liu, L., & Cribb, M. (2020). Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches. Remote Sensing of Environment, 248, 112005 is available at https://dx.doi.org/10.1016/j.rse.2020.112005.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Wei_Cloud_Detection_Landsat.pdfPre-Published version3.26 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

91
Last Week
0
Last month
Citations as of Mar 24, 2024

Downloads

49
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

58
Citations as of Mar 28, 2024

WEB OF SCIENCETM
Citations

46
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.