Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks-Reference-Cited by-同舟云学术

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Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks

Published:2024-06 Issue: Volume:212 Page:73-95
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Che Xianghong,Zhang Hankui K.,Li Zhongbin B.,Wang Yong,Sun Qing,Luo Dong,Wang Hao

Funder

China Meteorological Administration

Chinese Academy of Surveying and Mapping

National Key Research and Development Program of China Stem Cell and Translational Research

Publisher

Elsevier BV

Reference124 articles.

1. A comparison of conventional and geostatistical methods to replace clouded pixels in NOAA-AVHRR images;Addink;Int. J. Remote Sens.,1999

2. A land use and land cover classification system for use with remote sensor data;Anderson,1976

3. A coastal band spectral combination for water body extraction using Landsat 8 images;Aroma;Int. J. Environ. Sci. Technol.,2024

4. AVHRR NDVI compositing method comparison and generation of multi-decadal time series—A TIMELINE thematic processor;Asam;Remote Sens. (Basel),2023

5. Cloud cover in Landsat observations of the Brazilian Amazon;Asner;Int. J. Remote Sens.,2001

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