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Improved Hyperspectral Vegetation Detection Using Neural Networks with Spectral Angle Mapper
Date
2017-04-13
Author
Özdemir, Okan Bilge
Çetin, Yasemin
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
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Hyperspectral images have been used in many areas including city planning, mining and military decision support systems. Hyperspectral image analysis techniques have a great potential for vegetation detection and classification with their capability to identify the spectral differences across the electromagnetic spectrum due to their ability to provide information about the chemical compositions of materials. This study introduces a vegetation detection method employing Artificial Neural Network (ANN) over hyperspectral imaging. The algorithm employed backpropagation MLP algorithm for training neural networks. The performance of ANN is improved by the joint use with Spectral Angle Mapper (SAM). The algorithm first obtains the certainty measure from ANN, following the completion of this process, every pixels' angular distance is computed by SAM. The certainty measure is divided by angular distance. Results from ANN, SAM and Support Vector Machine (SVM) algorithms are compared and evaluated with the result of the algorithm. Limited number of training samples are used for training. The results demonstrate that joint use of ANN and SAM significantly improves classification accuracy for smaller training samples
Subject Keywords
Hyperspectral
,
Classification
,
SVM
,
Neural Networks
,
Spectral Angle Mapper
URI
https://hdl.handle.net/11511/51834
DOI
https://doi.org/10.1117/12.2262480
Collections
Graduate School of Informatics, Conference / Seminar