Improving image classification accuracy: A method to incorporate uncertainty in the selection of training sample set

Luisa M. S. Gonçalves, Cidália C. Fonte, Hugo Carrão, Mário Caetano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The automatic production of land cover maps using multispectral remote sensing images requires the use of learning classifiers for mapping the imagery data into a set of discrete classes. A group of classifiers commonly used are the supervised classifiers. The first stage of a supervised classification consists on the identification of training areas in the satellite image for each class, which are then used as descriptors of the spectral characteristics of the different classes. The classification results are therefore influenced by the sample pixels selected as training sets. This paper proposes an automatic method to assist the selection of training samples for mapping land cover from satellite images with the aid of ancillary information, namely elder or contemporaneous maps with lower spatial resolution, the Normalized Difference Vegetation Index and information provided by the classification uncertainty. It is shown that more accurate outputs may be derived with this methodology and some conclusions are drawn.

Original languageEnglish
Title of host publicationAccuracy 2010
Subtitle of host publicationProceedings of the 9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences
Pages261-264
Number of pages4
Publication statusPublished - 1 Jan 2010
Event9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2010 - Leicester, United Kingdom
Duration: 20 Jul 201023 Jul 2010

Conference

Conference9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2010
CountryUnited Kingdom
CityLeicester
Period20/07/1023/07/10

Keywords

  • Classification accuracy
  • Measures of uncertainty
  • Soft classification
  • Training samples

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