Background In this paper, an unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV) over paddy fields. Unlike existing supervised learning methods that require a large amount of labeled training data, the unsupervised learning approach detects panicle pixels in UAV images by analyzing statistical properties of pixels in an image without a training phase. Under the Bayesian framework, the distributions of pixel intensities are assumed to follow a multivariate Gaussian mixture model (GMM), with different components in the GMM corresponding to different categories, such as panicle, leaves, or background. The prevalence of each category is characterized by the weights associated with each component in the GMM. The model parameters are iteratively learned by using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling, without the need of labeled training data. Results Applying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and F 1 score of 96.49%, 72.31%, and 82.10%, respectively. These numbers outperform existing supervised learning approaches. Conclusions Experimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.

中文翻译：

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利用无人机图像进行水稻穗分割的无监督贝叶斯学习。


背景本文提出了一种无监督贝叶斯学习方法，利用无人机（UAV）在稻田上拍摄的光学图像进行水稻穗分割。与需要大量标记训练数据的现有监督学习方法不同，无监督学习方法通​​过分析图像中像素的统计特性来检测无人机图像中的穗像素，而无需训练阶段。在贝叶斯框架下，假设像素强度的分布遵循多元高斯混合模型（GMM），GMM中的不同分量对应于不同的类别，例如圆锥花序、叶子或背景。每个类别的流行度由与 GMM 中每个组成部分相关的权重来表征。使用马尔可夫链蒙特卡罗（MCMC）方法和吉布斯采样迭代学习模型参数，无需标记训练数据。结果将无监督贝叶斯学习算法应用于不同的无人机图像，平均召回率、精度和F 1 分数分别为96.49%、72.31%和82.10%。这些数字优于现有的监督学习方法。结论 实验结果表明，该方法能够准确识别不同条件下拍摄的无人机图像中的穗像素。