Spectral-texture approach to hyperspectral image analysis for plant classification with SVMs

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.