Document Type
Article
Publication Version
Version of Record
Publication Date
12-2014
Description
Dimensionality reduction (DR) is a widely used technique to address the curse of dimensionality when high-dimensional remotely sensed data, such as multi-temporal or hyperspectral imagery, are analyzed. Nonlinear DR algorithms, also referred to as manifold learning algorithms, have been successfully applied to hyperspectral data and provide improved performance compared with linear DR algorithms. However, DR algorithms cannot handle missing data that are common in multi-temporal imagery. In this paper, the Laplacian Eigenmaps (LE) nonlinear DR algorithm was refined for application to multi-temporal satellite data with large proportions of missing data. Refined LE algorithms were applied to 52-week Landsat time series for three study areas in Texas, Kansas and South Dakota that have different amounts of missing data and land cover complexity. A series of random forest classifications were conducted on the refined LE DR bands using varying proportions of training data provided by the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL); these classification results were compared with conventional metrics-based random forest classifications. Experimental results show that compared with the metrics approach, higher per-class and overall classification accuracies were obtained using the refined LE DR bands of multispectral reflectance time series, and the number of training samples required to achieve a given degree of classification accuracy was also reduced. The approach of applying the refined LE to multispectral reflectance time series is promising in that it is automated and provides dimensionality-reduced data with desirable classification properties. The implications of this research and possibilities for future algorithm development and application are discussed.
Publication Title
Remote Sensing of Environment
Volume
158
First Page
478
Last Page
491
DOI of Published Version
10. 1016/j.rse.2014.11.024
Language
en
Publisher
Elsevier
Rights
Copyright © The Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Roy, David P. and Yan, L., "Improved Time Series Land Cover Classification by Missing-Observation-Adaptive Nonlinear Dimensionality Reduction" (2014). GSCE Faculty Publications. 44.
https://openprairie.sdstate.edu/gsce_pubs/44