Bosilj Petra, Duckett Tom, Cielniak Grzegorz
Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, UK.
Comput Ind. 2018 Jun;98:226-240. doi: 10.1016/j.compind.2018.02.003.
Discriminating value crops from weeds is an important task in precision agriculture. In this paper, we propose a novel image processing pipeline based on attribute morphology for both the segmentation and classification tasks. The commonly used approaches for vegetation segmentation often rely on thresholding techniques which reach their decisions globally. By contrast, the proposed method works with connected components obtained by image threshold decomposition, which are naturally nested in a hierarchical structure called the max-tree, and various attributes calculated from these regions. Image segmentation is performed by attribute filtering, preserving or discarding the regions based on their attribute value and allowing for the decision to be reached locally. This segmentation method naturally selects a collection of foreground regions rather than pixels, and the same data structure used for segmentation can be further reused to provide the features for classification, which is realised in our experiments by a support vector machine (SVM). We apply our methods to normalised difference vegetation index (NDVI) images, and demonstrate the performance of the pipeline on a dataset collected by the authors in an onion field, as well as a publicly available dataset for sugar beets. The results show that the proposed segmentation approach can segment the fine details of plant regions locally, in contrast to the state-of-the-art thresholding methods, while providing discriminative features which enable efficient and competitive classification rates for crop/weed discrimination.
在精准农业中，区分经济作物和杂草是一项重要任务。在本文中，我们提出了一种基于属性形态学的新颖图像处理流程，用于分割和分类任务。常用的植被分割方法通常依赖于全局阈值技术来做出决策。相比之下，我们提出的方法处理通过图像阈值分解得到的连通分量，这些连通分量自然地嵌套在一个称为最大树的层次结构中，并基于这些区域计算各种属性。图像分割通过属性过滤来执行，根据区域的属性值保留或丢弃区域，从而实现局部决策。这种分割方法自然地选择了一组前景区域而非像素，并且用于分割的相同数据结构可进一步用于提供分类特征，在我们的实验中通过支持向量机（SVM）来实现。我们将我们的方法应用于归一化差异植被指数（NDVI）图像，并在作者在洋葱田收集的数据集以及一个公开可用的甜菜数据集上展示了该流程的性能。结果表明，与现有阈值方法相比，所提出的分割方法能够在局部对植物区域的精细细节进行分割，同时提供有区分性的特征，从而实现高效且具有竞争力的作物/杂草分类准确率。
