Engineering topics

Degloria, Stephen D.

Publications and source records attributed to Degloria, Stephen D..

3 records

NASA NTRS1987

Gaussian maximum likelihood and contextual classification algorithms for multicrop classification

The paper reviews some of the ways in which context has been handled in the remote-sensing literature, and additional possibilities are introduced. The problem of computing exhaustive and normalized class-membership probabilities from the likelihoods provided by the Gaussian maximum likelihood classifier (to be used as initial probability estimates to start relaxation) is discussed. An efficient implementation of probabilistic relaxation is proposed, suiting the needs of actual remote-sensing applications. A modified fuzzy-relaxation algorithm using generalized operations between fuzzy sets is presented. Combined use of the two relaxation algorithms is proposed to exploit context in multispectral classification of remotely sensed data. Results on both one artificially created image and one MSS data set are reported.

Di Zenzo, Silvano↗

NASA NTRS1987

Gaussian maximum likelihood and contextual classification algorithms for multicrop classification experiments using thematic mapper and multispectral scanner sensor data

The paper presents the results of a four-factor two-level analysis of a variance experiment designed to evaluate the combined effect of the improved quality of remote-sensor data and the use of context by the classifier on classification accuracy. The improvement achievable by using the context via relaxation techniques is significantly smaller than that provided by an increase of the radiometric resolution of the sensor from 6 to 8 bits per sample (the relative increase in radiometric resolution of TM relative to MSS). It is almost equal to that achievable by an increase in the spectral coverage as provided by TM relative to MSS.

Di Zenzo, Silvano↗

NASA NTRS1986

Discrimination of natural and cultivated vegetation using Thematic Mapper spectral data

The availability of high quality spectral data from the current suite of earth observation satellite systems offers significant improvements in the ability to survey and monitor food and fiber production on both a local and global basis. Current research results indicate that Landsat TM data when used in either digital or analog formats achieve higher land-cover classification accuracies than MSS data using either comparable or improved spectral bands and spatial resolution. A review of these quantitative results is presented for both natural and cultivated vegetation.

Degloria, Stephen D.↗