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

针对自然条件下光照条件变化给大田油菜图像分割带来的问题,该文研究了油菜图像的高斯HI颜色分割算法,为作物生长发育周期的自动识别提供前期准备。已有统计结果表明,在仅保留绿色作物的图像中,不同色调值的像素数量服从高斯分布。该文将去掉背景信息的样本数据从RGB颜色模型转换至HSI颜色模型后,统计各个光强的所有像素对应的色调值,并计算其期望值和方差,依次得出所有强度所对应色调值的期望值和方差,建立出油菜作物色调强度查找表(hue intensity-look up table)。在此基础上,计算每个像素的色调值和期望值之间的差值,若差值小于阈值,则像素被分割为作物,否则为背景。为了在高斯HI颜色分割算法中确定合适的阈值,该研究选取了45幅不同天气状况(晴天、阴天和雨天)不同发育阶段(苗期、三叶期和四叶期)的油菜图像作为样本,探讨阈值的选取与分割结果的关系。结果表明阈值在[2.4,2.6]内分割效果最佳,油菜目标的形状特征完整度最好。为了对图像分割结果进行评价,分别利用高斯HI颜色模型、CIVE(color index of vegetation extraction)、EXG-EXR(excess green-excess red)、EXG(excess green)和VEG(vegetation)算法对15幅不同天气状况的图像进行分割。从视觉效果上来看,高斯HI算法仅需少量样本,即可达到满意分割效果。与其他方法相比,高斯HI颜色分割算法的误分割率(misclassification error,ME)仅为1.8%,相对目标面积误差(relative object area error,RAE)仅为3.6%,均优于其他4种算法的试验结果。在分割结果稳定性上,高斯HI颜色算法表现最好,其ME和RAE值的标准差最低,分别为0.7%和4.5%。试验结果表明,高斯HI颜色算法能取得较好的分割效果,而且对光照条件变化并不敏感,同时,能够充分保留油菜形状特征的完整性,为后期油菜生长发育周期的自动识别提供可靠数据。

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.