A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico

Xingguang Yan; Jing Li; Di Yang; Jiwei Li; Tianyue Ma; Yiting Su; Jiahao Shao; Rui Zhang

doi:10.3390/rs14205154

Remote Sensing (Oct 2022)

A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico

Xingguang Yan,
Jing Li,
Di Yang,
Jiwei Li,
Tianyue Ma,
Yiting Su,
Jiahao Shao,
Rui Zhang

Affiliations

Xingguang Yan: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Jing Li: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Di Yang: Wyoming Geographic Information Science Center, University of Wyoming, Laramie, WY 82070, USA
Jiwei Li: Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA
Tianyue Ma: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Yiting Su: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Jiahao Shao: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Rui Zhang: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

DOI: https://doi.org/10.3390/rs14205154
Journal volume & issue: Vol. 14, no. 20
p. 5154

Abstract

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With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucatán Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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