A spatialized classification approach for land cover mapping using hyperspatial imagery
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Authors
Zou, Yi
Greenberg, Jonathan A.
Issue Date
2019
Type
Citation
Language
Keywords
Hyperspatial imagery , Image classification , Pattern recognition , Spatialized classification , HPC-Pronghorn
Alternative Title
Abstract
Maps of classified surface features are a key output from remote sensing. Conventional methods of pixel-based classification label each pixel independently by considering only a pixel's spectral properties. While these purely spectral-based techniques may be applicable to many medium and coarse-scale remote sensing analyses, they may become less appropriate when applied to high spatial resolution imagery in which the pixels are smaller than the objects to be classified. At this scale, there is often higher intra-class spectral heterogeneity than inter-class spectral heterogeneity, leading to difficulties in using purely spectral-based classifications. A solution to these issues is to use not only a pixel's spectral characteristics but also its spatial characteristics. In this study, we develop a generalizable "spatialized" classification approach for high spatial resolution image classification. We apply the proposed approach to map vegetation growth forms such as trees, shrubs, and herbs in a forested ecosystem in the Sierra Nevada Mountains. Our results found that the spatialized classification approach outperformed spectral-only approaches for all cover classes examined, with the largest improvements being in discriminating vegetation classes.
Description
Citation
Zou, Y., & Greenberg, J. A. (2019). A spatialized classification approach for land cover mapping using hyperspatial imagery. Remote Sensing of Environment, 232, 111248.
Publisher
License
In Copyright (All Rights Reserved)
Journal
Volume
Issue
PubMed ID
ISSN
0034-4257