Rule-Based Approach to the Automatic Detection of Individual Tree Crowns in RGB Satellite Images

Tanqiu Jiang, Ziyu Xiong
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Abstract

Along with the rapid growth of wildfire events around the globe, the appeal to a better forest management strategy is becoming increasingly stronger recently. “Tree Delineation”, which refers to the process of identifying each individual tree from images, is a crucial element in the fields of forest management and remote sensing. Many efforts have been done to locate each individual tree in an image, but the vast majority of the researches were not based on the RGB images that are the most common and the most easily available at a large scale. In our study, we used RGB satellite images from Google Earth and attempted to identify each tree in the images with a rule-based methodology. Our method involves steps including recognizing vegetation, isolating trees, and locating local maxima. The result of our algorithm is comparable to labeling trees manually, and the robustness was confirmed by repeating the same approach on multiple images of different locations.
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基于规则的RGB卫星图像单个树冠自动检测方法
随着全球野火事件的快速增长,对更好的森林管理战略的呼吁越来越强烈。“树木圈定”是指从图像中识别每棵树的过程,是森林管理和遥感领域的一个关键要素。已经做了很多努力来定位图像中的每棵树,但是绝大多数的研究都不是基于最常见和最容易大规模获得的RGB图像。在我们的研究中,我们使用了来自Google Earth的RGB卫星图像,并试图用基于规则的方法识别图像中的每棵树。我们的方法包括识别植被、隔离树木和定位局部最大值等步骤。我们的算法的结果与手动标记树相当,并且通过在不同位置的多幅图像上重复相同的方法来验证鲁棒性。
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