Abstract
A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons.
| Original language | English |
|---|---|
| Article number | 6172550 |
| Pages (from-to) | 963-967 |
| Number of pages | 5 |
| Journal | IEEE Geoscience and Remote Sensing Letters |
| Volume | 9 |
| Issue number | 5 |
| DOIs | |
| State | Published - 2012 |
Funding
Manuscript received August 25, 2011; revised December 3, 2011; accepted January 2, 2012. Date of publication March 21, 2012; date of current version May 29, 2012. This work was supported by the National Aeronautics and Space Administration under Grant NNS06AA98B and the National Oceanic and Atmospheric Administration under Grant NA07OAR4170517.
Keywords
- Clustering
- feature extraction
- satellite precipitation estimation (SPE)
- self-organizing map
- unsupervised feature selection