Multispectral data processing procedures are outlined beginning with the data display process used to accomplish data editing and proceeding through clustering, feature selection criterion for error probability estimation, and sample clustering and sample classification. The effective utilization of large quantities of remote sensing data by formulating a three stage sampling model for evaluation of crop acreage estimates represents an improvement in determining the cost benefit relationship associated with remote sensing technology.
Data processing 1: Advancements in machine analysis of multispectral data
1972-01-21
Conference paper
No indication
English
NTRS | 1977
Multispectral Data Analysis Final Report
NTIS | 1970
|Multispectral data analysis Final report
NTRS | 1970
|