Name
Abstract | ||
|---|---|---|
This paper investigates whether the combination of airborne hyperspectral imagery (Aisa EAGLE II) and image classification methods (MLC, SVM) using feature extraction can discriminate among species and clones of energy trees. The trees examined have similar morphological traits due to limitation of detection. The image classification was applied on a spectrally selected and transformed (PCA, MNF) dataset. A binary tree SVM classifier was developed in accordance with the principle of SVM, based on the Jeffries-Matusita (JM) separability measure of selected classes. The adaptive binary tree SVM on MNF-transformed dataset provided more accurate results than applied MLC and multiclass SVM methods. The primary outcome of this study was a comparison of support vector machines (SVM) classification methods to evaluate species or clones of energy plants. In this paper, an adaptive binary tree SVM classifier (ABTSVM) is proposed to increase the accuracy of subspecies level. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WHISPERS.2014.8077499 | 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | Field | DocType |
SVM,feature extraction,hyperspectral remote sensing,image classification,Aisa EAGLE | Structured support vector machine,Data mining,Pattern recognition,Tree species,Support vector machine,Binary tree,Hyperspectral imaging,Feature extraction,Artificial intelligence,Svm classifier,Contextual image classification,Mathematics | Conference |
ISSN | ISBN | Citations |
2158-6268 | 978-1-4673-9013-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| p burai | 1 | 10 | 2.35 |
| Laszlo Beko | 2 | 0 | 0.34 |
| c lenart | 3 | 0 | 1.01 |
| Tamás Tomor | 4 | 0 | 0.34 |