Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV
Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV
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DOI：
10.3390/rs61212037
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发表时间：
2014-12-01
期刊：
影响因子：
5
通讯作者：
Sukkarieh, Salah
中科院分区：
文献类型：
--
作者：
Hung, Calvin;Xu, Zhe;Sukkarieh, Salah
低成本无人机（uav）和轻型成像传感器的发展已经引起了人们对其用于遥感应用的极大兴趣。虽然对小型无人机收集的数据的收集、校准、配准和拼接已经引起了人们的极大关注，但将这些数据解释为语义上有意义的信息仍然是一项艰巨的任务。标准的数据收集和分类工作流程需要大量的人工工作来调整片段大小、特征选择和基于规则的分类器设计。在本文中，我们提出了一种替代的基于学习的方法，使用特征学习来最大限度地减少所需的人工工作量。我们将该系统应用于入侵杂草的分类。小型无人机适合这种应用，因为它们可以以高空间分辨率收集数据，这对于小型或局部杂草爆发的分类至关重要。在本文中，我们应用特征学习来生成一组图像过滤器，这些过滤器允许提取区分感兴趣的杂草和背景对象的特征。这些特征汇集起来汇总图像统计数据，并形成基于文本的线性分类器的输入，该分类器将图像补丁分类为杂草或背景。我们对澳大利亚的三种重要杂草：水葫芦、热带苹果和锯齿tussock进行了评估。我们的研究结果表明，在5-10 m处采集图像导致分类器准确率最高，F1得分高达94%。
The development of low-cost unmanned aerial vehicles (UAVs) and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from small UAVs, the interpretation of these data into semantically meaningful information can still be a laborious task. A standard data collection and classification work-flow requires significant manual effort for segment size tuning, feature selection and rule-based classifier design. In this paper, we propose an alternative learning-based approach using feature learning to minimise the manual effort required. We apply this system to the classification of invasive weed species. Small UAVs are suited to this application, as they can collect data at high spatial resolutions, which is essential for the classification of small or localised weed outbreaks. In this paper, we apply feature learning to generate a bank of image filters that allows for the extraction of features that discriminate between the weeds of interest and background objects. These features are pooled to summarise the image statistics and form the input to a texton-based linear classifier that classifies an image patch as weed or background. We evaluated our approach to weed classification on three weeds of significance in Australia: water hyacinth, tropical soda apple and serrated tussock. Our results showed that collecting images at 5-10 m resulted in the highest classifier accuracy, indicated by F1 scores of up to 94%.