融合CLAHE-SV增强Lab颜色特征的水稻覆盖度提取

为解决由于阈值不确定和光照强度不稳定所造成的植被覆盖度提取效果不理想的问题，该研究提出一种融合CLAHE-SV（contrast limited adaptive histogram equalization-saturation value）增强Lab颜色空间特征的高斯混合模型聚类算法。以分蘖后期的水稻为对象，利用无人机获取2、3、4和5 m高度下的水稻可见光图像，采用限制对比度自适应直方图均衡化算法（contrast limited adaptive histogram equalization，CLAHE）对HSV颜色空间中饱和度（S）和亮度（V）分量进行增强，并在此基础上应用高斯混合模型（gaussian mixture model，GMM）结合Lab颜色空间的a分量分割图像背景和提取水稻覆盖度，并与GMM-RGB、GMM-HSV、GMM-Lab进行对比分析。结果表明，基于a分量构建的GMM-CLAHE-SV-a与GMM-a模型在不同高度图像中的分割效果均优于RGB、HSV、Lab，其中GMM-CLAHE-SV-a精度最佳。相比于GMM-a，在高度为2 、3 、4 和5 m时GMM-CLAHE-SV-a的总体分割精度均值分别提高了2.16、1.01、1.03和1.26个百分点，Kappa系数均值分别提高了0.0414、0.0173、0.0190和0.0221；覆盖度的平均提取误差分别降低了8.75、7.01、5.93和5.34个百分点，决定系数R²分别提高了0.0960、0.0502、0.0622和0.1906，较好地降低了光强和倒影的影响。与已有方法相比，该算法无需标记训练集或计算阈值，可直接对无人机图像进行处理，具有较高的普适性，可以在复杂的大田环境下快速分割水稻像素并提取植被覆盖度信息。

Unsupervised extraction of rice coverage with incorporating CLAHE-SV enhanced Lab color features

Fractional vegetation cover (FVC) is an essential agronomic index. Quick and accurate acquisition of coverage is very important for the real-time monitoring of crop growth status in breeding and precision agricultural management. Image background segmentation is the key step in canopy cover extraction, and accurately segmenting the image from the background can effectively reduce the error of canopy cover extraction. The performance of traditional segmentation methods largely depends on the quality of the training data set, and is easily affected by the changes in segmentation thresholds and lightintensity during the different growth periods of crops, resulting in image segmentation method with low accuracy anduniversality, which ultimately leads to the problem of unsatisfactory vegetation cover extraction. In order to solve the above issues, in this study, Gaussian Mixture Model clustering was proposed using the Lab color space features enhanced by CLAHE-SV (contrast limited adaptive histogram equalization-saturation value). Taking rice at the late tillering stage as the object, the visible images of rice at 2, 3, 4, and 5 m height were collected by unmanned aerial vehicle (UAV). The saturation (S) and Value (V) components in HSV color space were enhanced by contrast limited adaptive histogram equalization algorithm (CLAHE). Gaussian Mixture Model (GMM) combined with the a-component of Lab color space was applied to segment the image background and extract the rice coverage, and then compared with the GMM-RGB, GMM-HSV, GMM-Lab, and GMM-a. The results show that the two GMM models with the a-component shared a better performance of segmentation than RGB, HSV, and Lab at different heights, where the accuracy of GMM-CLAHE-SV-a was the best. Compared with the GMM-a, the average overall accuracy of GMM-CLAHE-SV-a with image segmentation increased by 2.16, 1.01, 1.03, and 1.26 percentage points, respectively, while the average Kappa coefficient increased by 0.0414, 0.0173, 0.0190, and 0.0221, respectively, at heights of 2, 3, 4, and 5 m; The average extraction error of coverage decreased by 8.75, 7.01, 5.93, and 5.34 percentage points, respectively, whereas, the fitting accuracies were improved by 0.0960, 0.0502, 0.0622, and 0.1906, respectively, at heights of 2, 3, 4, and 5 m. The image segmentation and coverage extraction performance of GMM-CLAHE-SV-a were superior to GMM-a, thus effectively reducing the influence of light intensity and reflection. UAV images can be directly processed without labeling the training set or thresholds. The high universality of the improved model can also be expected to quickly segment the rice pixels and extract the fractional vegetation cover information in complex field environments.