Engineering topics

Bartolucci, L.

Publications and source records attributed to Bartolucci, L..

3 records

NASA NTRS1984

LANDSAT-4 MSS and Thematic Mapper data quality and information content analysis

LANDSAT-4 thematic mapper (TM) and multispectral scanner (MSS) data were analyzed to obtain information on data quality and information content. Geometric evaluations were performed to test band-to-band registration accuracy. Thematic mapper overall system resolution was evaluated using scene objects which demonstrated sharp high contrast edge responses. Radiometric evaluation included detector relative calibration, effects of resampling, and coherent noise effects. Information content evaluation was carried out using clustering, principal components, transformed divergence separability measure, and supervised classifiers on test data. A detailed spectral class analysis (multispectral classification) was carried out to compare the information content of the MSS and TM for a large number of scene classes. A temperature-mapping experiment was carried out for a cooling pond to test the quality of thermal-band calibration. Overall TM data quality is very good. The MSS data are noisier than previous LANDSAT results.

Anuta, P.↗

NASA NTRS1984

LANDSAT-4 MSS and TM Spectral Class Comparison and Coherent Noise Analysis

A detailed spectral analysis is conducted of thematic mapper and MSS data for an area near Des Moines, Iowa. Data are utilized from 7 blocks distributed throughout the area which included agricultural, forest, suburan, urban, and water scene types. The blocks are processed using a clustering algorithm to produce up to 18 cluster groupings for each block. Each cluster class is then identified with a ground-cover class using aerial photography and maps of the area. The clusters from each of the 7 blocks are inspected with regard to separability, mean, and variances. The separability measure used in the transformed divergence function or processor measures the statistical distance between classes based on class means and covariance matrices. The measure has a maximum value of 2,000 and the minimum of 0. Spectrally, very close classes will typically have values as low as 50 to 500.

Anuta, P. E.↗

NASA NTRS1984

Landsat-4 data quality analysis

Landsat-4 satellite Thematic Mapper (TM) and multispectral scanner (MSS) data have been analyzed in order to ascertain data quality and information content. Geometric evaluations have tested band-to-band registration accuracy, and the TM's overall system resolution was evaluated for the case of image objects with high contrast, sharp edge responses. The information content evaluation employed clustering, principal components, and the transformed divergence separability measured on data from Iowa and Chicago, Illinois. The MSS classification analysis compared MSS and TM information contents for a large number of science classes.

Anuta, P.↗