Confusion matrices evaluating the accuracy of supervised classification of habitat types in UAV aerial photos around American kestrel nest sites in Massachusetts, USA

DOI

Confusion matrices generated by program ENVI to evaluate the accuracy of Supervised Classification via a Maximum Likelihood method. Each of 12 sites was photographed at 25m and 50m heights by a Phantom 2 Vision+ quadcopter drone. Each 50m photo was also cropped to the same field of view as the 25m photo in order to examine effects of changes in image resolution with altitude. At 25m and 50m heights, different final image resolutions (kernel sizes, in pixels) were also recorded to compare. Each image was classified into a maximum of five different cover types, and the number of pixels correctly and incorrectly assigned to each cover category is recorded in the confusion matrix.

Identifier
DOI https://doi.org/10.1594/PANGAEA.884660
Related Identifier https://doi.org/10.1594/PANGAEA.884669
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.884660
Provenance
Creator Kamm, Matthew; Reed, J Michael
Publisher PANGAEA
Contributor Tufts University
Publication Year 2018
Rights Creative Commons Attribution 3.0 Unported; https://creativecommons.org/licenses/by/3.0/
OpenAccess true
Representation
Resource Type Dataset
Format application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Size 44.7 kBytes
Discipline Earth System Research
Spatial Coverage (-71.530 LON, 42.230 LAT); United States