Работы, цитирующие эту работу

Работ: 5

Работа: Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data

1. Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000–2012

   Christopher Conrad, Sarah Schönbrodt‐Stitt, Fabian Löw +2

   СтатьяRemote Sensing in AgricultureRemote Sensing2016Цитирований: 8

   ABIABI:AkademIndex/openalex/2016.article.000040

2. Classification of seasonal dynamics of soil moisture according to satellite data Sentinel-2, Jizzakh region, Uzbekistan

   Sanjar Rakhmonov, А. А. Хайдаров, Kanat Samarkhanov +3

   СтатьяEngineering and Agricultural InnovationsE3S Web of Conferences2023Цитирований: 1

   ABIABI:AkademIndex/openalex/2023.article.003137

3. Advances and Prospects in Machine Learning for GIS and Remote Sensing: A Comprehensive Review of Applications and Research Frontiers

   Nozimjon Teshaev, Bobomurod Makhsudov, Izzatilla Ikramov +1

   Обзорная статьяRemote Sensing in AgricultureE3S Web of Conferences2024Цитирований: 1

   ABIABI:AkademIndex/openalex/2024.review-article.000072

4. Towards improved land use mapping of irrigated croplands: performance assessment of different image classification algorithms and approaches

   Amit Kumar Basukala, Carsten Oldenburg, J. Schellberg +2

   СтатьяRemote Sensing in AgricultureEuropean Journal of Remote Sensing2017Цитирований: 0

   ABIABI:AkademIndex/openalex/2017.article.000037

5. SAR and optical time series for crop classification

   Dimo Dimov, Fabian Löw, Mirzahayot Ibrakhimov +2

   СтатьяSpectroscopy and Chemometric Analyses2017Цитирований: 0

   ABIABI:AkademIndex/openalex/2017.article.000143