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Rice Canopy Image Segmentation Using Support Vector Machine and Otsus Method
- HUANG Qiaoyi1,2, ZHANG Mu1, LI Ping1, FU Hongting1, HUANG Xu1,TANG Shuanhu1*
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2019, 21(4):
52-60.
DOI: 10.13304/j.nykjdb.2018.0160
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In order to resolve the difficult problem of rice canopy image segmentation caused by variable light intensities in the fields, this paper discussed a rice canopy image segmentation algorithm based on support vector machine (SVM) and maximum variance between classes (OTSU). Firstly, 2 class training images (rice S1 and background S2) were captured from the rice canopy images under different illumination conditions, and the density of R, G, B, r, g, b, L*, a*, b*, H, S, and V color indices from RGB, rgb(standard RGB), CIE L*a*b* and HSV color spaces for rice (S1) and background (S2) training images were analyzed. The g, a*, b*, and S color indices, whose histogram distributions of S1 and S2 were significant bimodal, were selected as key color indices. Afterwards, an optimal hyperplane Z（Z=0.421g+0.753a*+0.152b*+0.051S+0.085） in multi\|dimensional color spaces including g, a*, b*, and S color indices were obtained by support vector machine. Finally, the Z(x, y) of any rice canopy image was calculated, and threshold Zt value was calculated by OTSU method. The accuracy and robustness of the proposed method was examined over 90 test images under different illumination conditions (overcast days, cloudy days and sunny days), and compared with the performances of ExG & OTSU. The accuracy of segmentation and light intensity robustness of this method were analyzed. The results showed that the accuracy of ExG & OTSU method was influenced significantly by light intensity, and decreased along with the light intensity increasing. The robustness of light intensity was poor. The segmented error of the proposed method for the images, taken on overcast days, cloudy days, and sunny days were 7.30%, 8.72%, and 8.98%, respectively. The accuracy of segmentation was higher and with better robustness of light intensity. Therefore, the proposed method had higher segmentation accuracy and robustness of light intensity, and could provide technical reference for accurate segmentation of rice canopy image in fields under changeable illumination.
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