[Cotton identification and extraction using near infrared sensor and object-oriented spectral segmentation technique].
The real-time, effective and reliable method of identifying crop is the foundation of scientific management for crop in the precision agriculture. It is also one of the key techniques for the precision agriculture. However, this expectation cannot be fulfilled by the traditional pixel-based information extraction method with respect to complicated image processing and accurate objective identification. In the present study, visible-near infrared image of cotton was acquired using high-resolution sensor. Object-oriented segmentation technique was performed on the image to produce image objects and spatial/spectral features of cotton. Afterwards, nearest neighbor classifier integrated the spectral, shape and topologic information of image objects to precisely identify cotton according to various features. Finally, 300 random samples and an error matrix were applied to undertake the accuracy assessment of identification. Although errors and confusion exist, this method shows satisfying results with an overall accuracy of 96.33% and a KAPPA coefficient of 0.926 7, which can meet the demand of automatic management and decision-making in precision agriculture.
Deng
Jin-Song
JS
Institute of Remote Sensing & Information Technique, Zhejiang University, Hangzhou 310029, China. jsong_deng@zju.edu.cn
Shi
Yuan-Yuan
YY
Chen
Li-Su
LS
Wang
Ke
K
Zhu
Jin-Xia
JX
D002623:Chemistry Techniques, Analytical / Q000295:instrumentation*; D003368:Gossypium / Q000737:chemistry* / Q000145:classification; D007091:Image Processing, Computer-Assisted; D007259:Infrared Rays; D015203:Reproducibility of Results; D013997:Time Factors