Abstract
Numerous environmental and financial benefits of using hyperspectral imaging have driven much increased applications on plant monitoring and diagnosis. This paper is concerned with analysis of hyperspectral images for plant discrimination by means of their spectral and texture properties. The main contribution of the work lies in the use feature selection and Markov random field model (MRF) to facilitate such spectral-texture analysis to enhance prediction performance, as compared to conventional analysis methods. A hyperspectral dataset on control and stressed Arabidopsis plant leaves captured by a proximal hyperspectral imaging system was used in the experiment. Texture parameters with different orders were estimated from the MRF model and two support vector machine settings were used in the evaluation. Experimental results showed significant improvements in classification performance of the proposed spectral-texture approach over the conventional analysis methods.
| Original language | Undefined |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Imaging Systems and Techniques (IST) |
| Pages | 1-6 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 1 Oct 2017 |
Keywords
- Markov processes
- feature extraction
- image classification
- image texture
- support vector machines
- Arabidopsis plant
- MRF model
- Markov random field model
- hyperspectral dataset
- hyperspectral image analysis
- hyperspectral images
- plant classification
- plant discrimination
- plant monitoring
- proximal hyperspectral imaging system
- spectral texture properties
- spectral-texture analysis
- spectral-texture approach
- Feature extraction
- Hyperspectral imaging
- Imaging
- Maximum likelihood estimation
- Support vector machines
- Training
- feature selection
- hyperspectral imaging
- markov random field
- spectral analysis
- texture analysis