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Recent HARVIST Results: Classifying Crops from Remote Sensing DataIn this paper, we report on recent results from the Heterogeneous Agricultural Research Via Interactive, Scalable Technology (HARVIST) project. HARVIST seeks to provide the tools and scalability required to enable practicioners to analyze large, diverse data sets that may come from different data sources. We have focused on agricultural applications, and our current results demonstrate the ability of the system to train a crop type classifier that operates on orbital remote sensing images. We find that this classifier can label crops with an accuracy of 82%, comparable to other published results.
Document ID
20060050280
Acquisition Source
Jet Propulsion Laboratory
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Wagstaff, Kiri
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Mazzoni, Dominic
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 23, 2013
Publication Date
January 1, 2006
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: 2nd NASA Data Mining Workshop
Location: Pasadena, CA
Country: United States
Start Date: May 23, 2006
End Date: May 24, 2006
Distribution Limits
Public
Copyright
Other
Keywords
support vector machines
remote sensing
crop classifications

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