Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data.
Qinchuan XinJing LiZiming LiYaoming LiXuewen ZhouPublished in: Int. J. Appl. Earth Obs. Geoinformation (2020)
Keyphrases
- remote sensing
- land cover
- machine learning
- machine learning methods
- satellite data
- data driven
- benchmark datasets
- statistical methods
- empirical studies
- machine learning algorithms
- computer vision
- significant improvement
- information extraction
- knowledge acquisition
- unsupervised learning
- preprocessing
- satellite images
- remote sensing images
- information retrieval
- data mining