A support vector machine to identify irrigated crop types using time-series Landsat NDVI data

By B. Zheng, Soe Win Myint, Prasad S. Thenkabail, R. Aggarwal. International Journal of Applied Earth Observation and Geoinformation, 2015

340 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/c260391af73dfef0953401be3cbeed6350faeac9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Marco Körner
2. Marc Rußwurm

In Ecology

1. Marco Körner
2. Marc Rußwurm
3. Yuan Yuan
4. Lei Lin

In Media Technology

1. Marco Körner
2. Marc Rußwurm
3. Yuan Yuan
4. Lei Lin

Citations in the corpus

• Self-Attention for Raw Optical Satellite Time Series Classification (2019, methodology): “, with Random Forest Classifiers [7, 10, 6] or Support Vector Machines [11, 12, 13], as summarized by [14, 15].”
• Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data (2015, background): “Table 3 shows the overview of a few recent examples from the literature with key highlights where machine-learning classifiers were used for spaceborne remote sensing image classification [63–72].”