Quality assessment of heterogeneous training data sets for classification of urban area with land sat imagery
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Date
2020
Authors
Lyimo, Neema Nicodemus
Luo, Fang
Cheng, Qimin
Peng, Hao
Journal Title
Journal ISSN
Volume Title
Publisher
American society for photogrammetry and remote sensing
Abstract
Quality assessment of training samples collected from hetero-
geneous sources has received little attention in the existing
literature. Inspired by Euclidean spectral distance metrics,
this article derives three quality measures for modeling uncer-
tainty in spectral information of open-source heterogeneous
training samples for classification with Landsat imagery. We
prepared eight test case data sets from volunteered geo-
graphic information and open government data sources to
assess the proposed measures. The data sets have significant
variations in quality, quantity, and data type. A correlation
analysis verifies that the proposed measures can successfully
rank the quality of heterogeneous training data sets prior to
the image classification task. In this era of big data, pre-
classification quality assessment measures empower research
scientists to select suitable data sets for classification tasks
from available open data sources. Research findings prove the
versatility of the Euclidean spectral distance function to de-
velop quality metrics for assessing open-source training data
sets with varying characteristics for urban area classification.
Description
Journal Article
Keywords
Urban area, Landsat Imagery, Data Sets