Tree detection from aerial imagery

13 years 11 months ago

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We propose an automatic approach to tree detection from aerial imagery. First a pixel-level classiﬁer is trained to assign a {tree, non-tree} label to each pixel in an aerial image. The pixel-level classiﬁcation is then reﬁned by a partitioning algorithm to a clean image segmentation of tree and non-tree regions. Based on the reﬁned segmentation results, we adopt template matching followed by greedy selection to locate individual tree crowns. As training a pixel-level classiﬁer requires manual generation of ground-truth tree masks, we propose methods for automatic model and training data selection to minimize the manual work and scale the algorithm to the entire globe. We test the algorithm on thousands of production aerial images across diﬀerent countries. We demonstrate high-quality tree detection results as well as good scalability of the proposed approach. Categories and Subject Descriptors I.4 [Image Processing And Computer Vision]: Enhancement General Terms Algorithm...

Lin Yang, Xiaqing Wu, Emil Praun, Xiaoxu Ma

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