A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and la...

Full beskrivning

Bibliografiska uppgifter
Huvudupphovsmän: Nishanta Khanal, Mir Abdul Matin, Kabir Uddin, Ate Poortinga, Farrukh Chishtie, Karis Tenneson, David Saah
Materialtyp: Artikel
Språk:English
Publicerad: MDPI AG 2020-09-01
Serie:Remote Sensing
Ämnen:
remote sensing
temporal smoothing
google earth engine
machine learning
random forest
land cover
Länkar:https://www.mdpi.com/2072-4292/12/18/2888

Internet

https://www.mdpi.com/2072-4292/12/18/2888

Liknande verk