Analysis of Morphology-Based Features for Classification of Crop and Weeds in Precision Agriculture
Analysis of Morphology-Based Features for Classification of Crop and Weeds in Precision Agriculture
复制标题
DOI：
10.1109/lra.2018.2848305
复制
发表时间：
2018-10-01
影响因子：
5.2
通讯作者：
Cielniak, Grzegorz
中科院分区：
文献类型：
--
作者：
Bosilj, Petra;Duckett, Tom;Cielniak, Grzegorz
确定图像中存在的植被类型是许多精准农业任务的核心步骤。在这封信中，我们专注于基于像素的方法进行分类的作物与杂草，特别是对于复杂的情况下，涉及重叠的植物和部分遮挡。我们研究的好处多尺度和内容驱动的形态为基础的描述符称为属性配置文件。这些相比，国家的最先进的关键点描述符与以前在精准农业中使用的固定邻域，即直方图的梯度和本地二进制模式。建议的分类技术是特别有利的，再加上基于形态学的分割的最大树结构，作为相同的表示可以重复使用的特征提取。该方法的鲁棒性证明了两个数据集与不同的作物类型的实验评估，同时能够提供更高的分辨率的描述符。所提出的方法相比，有利的国家的最先进的方法，而不增加计算复杂性，同时能够提供更高的分辨率的描述符。
Determining the types of vegetation present in an image is a core step in many precision agriculture tasks. In this letter, we focus on pixel-based approaches for classification of crops versus weeds, especially for complex cases involving overlapping plants and partial occlusion. We examine the benefits of multiscale and content-driven morphology-based descriptors called attribute profiles. These are compared to the state-of-the-art keypoint descriptors with a fixed neighborhood previously used in precision agriculture, namely histograms of oriented gradients and local binary patterns. The proposed classification technique is especially advantageous when coupled with morphology-based segmentation on a max-tree structure, as the same representation can be reused for feature extraction. The robustness of the approach is demonstrated by an experimental evaluation on two datasets with different crop types, while being able to provide descriptors at a higher resolution. The proposed approach compared favorably to the state-of-the-art approaches without an increase in computational complexity, while being able to provide descriptors at a higher resolution.