Destroy this report when no longer needed. Do not return it to the originator.The findings in this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.The citation in this report of trade names of commercially available products does not constitute official endorsement or approval of the use of such products.
AD-A25 211 t 4ý,&q , outDer ocnse inludng he imeforT iC~*ng instrudion%. searchn e.4stinq data source,.
ABSTRACT (Maximum 200 words)Using digital elevation model data, landforms are classified into two broad, generic terrain features --mounts and non-mounts. Mount represents an aggregation of elevated features including hills, mountains and ranges. All remaining features are classified collectively as non-mount. The results of this work suggest that it may be possible to acceptably replicate the manual classification of certain generic terrain features. However, the general utility of the mount/ non-mount classification appears to be limited by the classificatinn algorithms, the nature of the regional terrain and the quality of available digital data. Possible applications for generic terrain feature information, such as mounts and non-mounts, are presened. ABSTRACT Using digital elevation model data, landforms are classified into two broad, generic terrain features --mounts and non-mounts.Mount represents an aggregation of elevated features including hills, mountains and ranges. All remaining features are classified collectively as nonmount.The results of this work suggest that it may be possible to acceptably replicate the manual classification of certain generic terrain features.However, the general utility of the mount/non-mount classification appears to be limited by the classification algorithms, the nature of the regional terrain and the quality of available digital data. Possible applications for generic terrain feature information, such as mounts and non-mounts, are presented.
oB No. 0704-0188 * , etman d to ae, aqe. o , c o u ,een. ,.ndud g the ton for rvenrq ,ntru •mlsb. search .ng-t n, data •o ,rcu1. ,nq an rev..n thse COIHt.On O• "fO'ma~tiO• $nd qOmm•,Yn c ien h th0 9.urden intlmt. or any Other asoec o4 this :Ing this buirdFen. tO W •%hn"tOn .4eadquare¢r S4•rvIcs. iOretorate Inf Ormato O1= 4t pe 'Of•m and port%. 12 IS jefle h 1 tO the Office of Managemen't and Sudgt. PaPerwork Reduction Pro,@" (0704-01) Washington, DC 2o0s0
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.