DocumentCode :
1759354
Title :
Logistic Regression-Based Spectral Band Selection for Tree Species Classification: Effects of Spatial Scale and Balance in Training Samples
Author :
Pant, P. ; Heikkinen, Ville ; Korpela, Ilkka ; Hauta-Kasari, Markku ; Tokola, Timo
Author_Institution :
Sch. of Comput., Univ. of Eastern Finland, Joensuu, Finland
Volume :
11
Issue :
9
fYear :
2014
fDate :
Sept. 2014
Firstpage :
1604
Lastpage :
1608
Abstract :
In this letter, we evaluated the pixel-level and plot-level tree species classification of Scots Pine, Norway Spruce, and deciduous birch in a boreal forest using 64-band AisaEAGLE II hyperspectral data in a wavelength range of 400-1000 nm. First, band selection was performed using a sparse logistic regression-based feature selection algorithm with pixel-level and plot-level data in case of balanced and imbalanced training data. This resulted in 8-11 selected hyperspectral bands, depending on the properties of the data used. We evaluated a tree species classification with 8-11 selected hyperspectral bands directly for a least squares support vector machine (LS-SVM)-based pixel-level classification with a relatively small training set size (0.5%-1.5% of the total data) and obtained an accuracy and kappa of around 93.50% and 0.90, respectively. These results are around 0.53%-0.94% points lower than those obtained using all of the hyperspectral bands. Second, one important wavelength region highlight by the selected bands was used to modify the sensor sensitivity configuration in the Leica Airborne Digital Sensor 40 (ADS40) multispectral sensor. Using a simulation model and the hyperspectral data, the modified and standard Leica ADS40 sensor responses were simulated and compared, and the modified system simulated response indicates a 3%-5% point improvement in the pixel-level and plot-level LS-SVM classification accuracy compared with the simulated responses of the standard Leica ADS40 band configuration.
Keywords :
geophysical image processing; image classification; regression analysis; support vector machines; vegetation mapping; AisaEAGLE II hyperspectral data; Leica ADS40 sensor; Leica Airborne Digital Sensor 40 multispectral sensor; Norway Spruce; Scots Pine; boreal forest; deciduous birch; least squares support vector machine; logistic regression based spectral band selection; pixel level tree species classification; plot level tree species classification; Accuracy; Hyperspectral imaging; Logistics; Sensitivity; Training; Vegetation; Band selection (BS); classification; feature selection; hyperspectral sensors; multispectral sensor; remote sensing;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2014.2301864
Filename :
6734669
Link To Document :
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