Date of Award
2019
Publisher
North Dakota State University
Document Type
Master's Paper
Degree Awarded
Master of Science (MS)
Program
Computer Science
Department
Computer Science
College
Engineering
Faculty Advisor
Denton, Anne
Description
Implications of artificial intelligence around the intersection of agricultural technology and satellite remote sensing are only beginning to emerge. One of the application areas in the agriculture sector that can leverage machine learning most immediately is land use classification of remotely sensed data. This research shows that window-based aggregates can help accomplish this task by creating extra layers of information in addition to the original satellite image. Sliding window-based aggregation allows creating these processed data layers efficiently. The results show that adding the processed layers to predictive machine learning models can boost classification accuracy.
Recommended Citation
Beshah, Dawit Mekonnen, "Evaluating Sliding Window Multi-Scalar Analysis Importance on Land Use Classification from Satellite Remote Sensing Data" (2019). Computer Science. 406.
https://digitalcommons.ndsu.edu/computer-sci/406
Handle Identifier
https://hdl.handle.net/10365/30216