Name
Title | ||
|---|---|---|
Robust spatial-spectral hyperspectral image classification for vegetation stress detection |
Abstract | ||
|---|---|---|
Hyperspectral imaging (HSI) techniques have been widely used for a variety of applications pertaining to vegetation species identification. With its rich spectral information, HSI is a powerful tool to detect and characterize vegetation species and their health. However, due to the high dimensionality of HSI, a the number of training samples required to estimate the parameters of the automated target recognition (ATR) or ground-cover classification algorithms is large. To avoid this over-dimensionality problem, feature selection or feature extraction must be performed to reduce the dimensionality of HSI data. This problem is further exacerbated when spatial information is also exploited in conjunction with spectral information. In this work, we propose a feature selection approach for extracting the most meaningful spatial and spectral features for a vegetative stress detection problem - genetic algorithms based linear discriminant analysis (GA-LDA). Experimental results show that applying GA with an appropriate fitness function in the spatial-spectral feature space is very effective at selecting the most pertinent features and yields very high classification accuracies. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6352364 | IGARSS |
Keywords | Field | DocType |
ground-cover classification algorithms,fitness function,spectral information,training samples,robust spatial-spectral hyperspectral image classification,over-dimensionality problem,spatial information,fisher's ratio,hyperspectral imagery,vegetative stress detection problem,linear discriminant analysis,hyperspectral imaging techniques,feature extraction,image classification,high classification accuracies,geophysical image processing,vegetation species identification,spatial-spectral feature space,hsi data,genetic algorithms,feature selection approach,vegetation mapping,automated target recognition,vegetation,accuracy,hyperspectral imaging,stress | Spatial analysis,Feature selection,Computer science,Remote sensing,Artificial intelligence,Contextual image classification,Computer vision,Feature vector,Pattern recognition,Feature extraction,Hyperspectral imaging,Linear discriminant analysis,Statistical classification | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4673-1158-8 | 978-1-4673-1158-8 | 1 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minshan Cui | 1 | 110 | 10.38 |
| Saurabh Prasad | 2 | 860 | 58.52 |
| Lori M. Bruce | 3 | 76 | 8.22 |
| Ramesh L. Shrestha | 4 | 12 | 5.43 |