Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

Abstract

The hyper-spectral image contains spectral and spatial information, which increases the ability and precision of objects classification. Despite the classification value of hyper-spectral imaging technology within various applications, users often find it difficult to effectively apply in practice because of the effect of light, temperature and wind in outdoor environment. This research presented a new classification model for outdoor farmland objects based on near-infrared (NIR) hyper-spectral images. It involves two steps including region of interest (ROI) acquisition and establishment of classifiers. A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition firstly. Then maximum likelihood (ML) and support vector machine (SVM) were used for farmland objects classification. The performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%, of which the SVM-M model could reach 99.5%. The research provided an effective method for outdoor farmland image classification. Keywords: distance-based separability criterion, near-infrared hyper-spectral image, ROI, farmland image classification DOI: 10.25165/j.ijabe.20171005.2264 Citation: Tang J L, Miao R H, Zhang Z Y, Xin J, Wang D. Distance-based separability criterion of ROI in classification of farmland hyper-spectral images. Int J Agric & Biol Eng, 2017; 10(5): 177–185.