NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Collections About News Help Login
Back to Results
An evaluation of ISOCLS and CLASSY clustering algorithms for forest classification in northern IdahoBoth the iterative self-organizing clustering system (ISOCLS) and the CLASSY algorithms were applied to forest and nonforest classes for one 1:24,000 quadrangle map of northern Idaho and the classification and mapping accuracies were evaluated with 1:30,000 color infrared aerial photography. Confusion matrices for the two clustering algorithms were generated and studied to determine which is most applicable to forest and rangeland inventories in future projects. In an unsupervised mode, ISOCLS requires many trial-and-error runs to find the proper parameters to separate desired information classes. CLASSY tells more in a single run concerning the classes that can be separated, shows more promise for forest stratification than ISOCLS, and shows more promise for consistency. One major drawback to CLASSY is that important forest and range classes that are smaller than a minimum cluster size will be combined with other classes. The algorithm requires so much computer storage that only data sets as small as a quadrangle can be used at one time.
Document ID
19820013776
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Werth, L. F. (Lockheed Engineering and Management Services Co., Inc. Houston, TX, United States)
Date Acquired
September 4, 2013
Publication Date
September 1, 1981
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
NAS 1.26:167447
NASA-CR-167447
E82-10109
RRI-L1-04143
LEMSCO-17154
JSC-17418
Accession Number
82N21650
Funding Number(s)
CONTRACT_GRANT: NAS9-15800
PROJECT: PROJ. AGRISTARS
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

Related Records

There are no records associated with this record.
No Preview Available