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 0.041 4, 0.017 3, 0.019 0, and 0.022 1, 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 0.096 0, 0.050 2, 0.062 2, and 0.190 6, 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.