4.7 Article

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

期刊

REMOTE SENSING
卷 14, 期 20, 页码 -

出版社

MDPI
DOI: 10.3390/rs14205154

关键词

Google Earth Engine; random forest; Landsat; NDWI; MNDWI; MODIS; Sentinel-2

资金

  1. National Natural Science Foundation of China [41501564]
  2. National Key Research and Development Program of China [2016YFC0501101-4]

向作者/读者索取更多资源

With the growth of cloud computing, the use of the Google Earth Engine platform for research based on Landsat images has become increasingly popular. This study focuses on the NDWI and MNDWI of the Yucatan Peninsula coastline from 1993 to 2021. The results show that the restored Landsat images provide improved results for studying water bodies and have a high correlation with Sentinel-2 data.
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 Yucatan 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据