农业工程学报2016，Vol.32Issue(8)：142-147,6.DOI:10.11975/j.issn.1002-6819.2016.08.020
基于高斯HI颜色算法的大田油菜图像分割
Segmentation of field rapeseed plant image based on Gaussian HI color algorithm
摘要
Abstract
Field crop image segmentation has drawn considerable attention in many aspects of agriculture, such as identification of physiological stage, disease, insect, and vegetation cover estimation. This research was conducted in order to achieve environmentally adaptive segmentation of field rapeseed plant and background. A digital camera which was mounted on a tripod that was around 1.5 m high, Canon EOS Digital Rebel XS, was utilized to take pictures of the field rapeseed plants. For the sake of continuous monitoring of rapeseed plant, the camera acquired the images 2 times per day. The fact that color distribution of a single-colored object in the hue-saturation (HS) plane is not invariant with brightness changes has been testified in several researches. Statistical results also showed that at a specified intensity, the histogram shape of hue was similar to the Gaussian distribution. Accordingly, the single Gaussian model was used to characterize the distribution of hue at certain intensity. Fifteen images under different illumination conditions, which changed from sunny days, cloudy days, to rainy days, were selected to establish the HI_LUT (hue intensity looking-up table). First, all the background was removed, and only the green pixels which represented rapeseed plants were kept. The green pixels in RGB (red, green, blue) color space were transformed into HSI (hue, saturation, intensity) color space. The expectation and variance values of hues were computed at certain intensity. As for one given pixel, if the distance between the hue value of that pixel and the expected hue was smaller than a certain threshold, the pixel was segmented as green crop. However, how to select an appropriate threshold value was a key problem that shall be solved, for different threshold value may give rise to different results. This paper selected 45 field rapeseed plant images under different illuminations (sunny days, cloudy days, and rainy days) and different physiological stages (seedling, three-leaf stage, and four-leaf stage) as the samples to discuss the relationship between the selection of the threshold value and the segmentation result. Different thresholdvalues were tested in order to find an appropriatethreshold. Results showed that the best segmentation results were achieved and the integrity of the shape characteristic of rapeseed plant target was kept when the threshold value ranged from 2.4 to 2.6. However, if the thresholdvalue equaled to 1.0, some green pixels were segmented as background. While the thresholdwas set as 4.0, non-green pixels were misclassified as rapeseed plants. In order to demonstrate the performance of the Gaussian HI algorithm, 4 established algorithms, namely, CIVE (color index of vegetation extraction), EXG-EXR (excess green - excess red), EXG (excess green) and VEG (vegetation) were implemented to make comparison with the Gaussian HI algorithm. Meanwhile, the ME (misclassification error) and RAE (relative objective area error) values were both calculated. Several conclusions could be drawn from the experimental results. 1) Good segmentation results could be achieved with the Gaussian HI algorithm with a few image samples. 2) The ME value of Gaussian HI algorithm reached 1.8%, while that of the other 4 algorithms were 2.7%, 3.8%, 3.1% and 4.2%, respectively. The RAE value was less than 3.6%, and that of the other 4 algorithms were 12.8%, 34.0%, 8.5% and 25.8%, respectively. 3) The standard deviation of the ME was 0.7%, and that of the RAE was 4.5%, which demonstrated that the algorithm showed better stability. The above test results verify the algorithm can segment the field rapeseed plant image effectively and guarantee the completeness of the crop shape, which can provide reliable database for the physiological stage identification of rapeseed plant.关键词
图像分割/算法/高斯分布/HSI颜色模型/大田油菜Key words
image segmentation/algorithms/Gaussian distribution/HI color models/field rapeseed plant分类
信息技术与安全科学引用本文复制引用
翟瑞芳,方益杭,林承达,彭辉,刘善梅,罗俊..基于高斯HI颜色算法的大田油菜图像分割[J].农业工程学报,2016,32(8):142-147,6.基金项目
华中农业大学中央高校基本科研业务费专项基金资助（2662015PY066）；国家自然科学基金（41301522）；湖北省自然科学基金（4006-36114052）。 （）
