基於增強型激光雷達偽標籤的圖像樹冠分割學習方法

摘要

單株樹冠描繪對於維護城市樹木清記錄和監測森林健康等任務至關重要，這些工作有助於我們理解並守護自然環境。然而由於樹冠紋理特徵及局部重疊等因素，從航空影像中自動分離相鄰樹冠極具挑戰性。本研究提出一種深度學習模型訓練方法，利用航空激光掃描（ALS）數據生成的偽標籤，實現從RGB和多光譜影像中分割識別單株樹木。實驗表明，通過零樣本實例分割模型SAM 2（Segment Anything Model 2）能有效提升ALS偽標籤的質量。該方法無需人工標註成本即可為光學影像模型提供領域專用的訓練標註，最終形成的分割模型在相同任務上的表現優於所有針對通用領域部署的現有模型。

English

Mapping individual tree crowns is essential for tasks such as maintaining urban tree inventories and monitoring forest health, which help us understand and care for our environment. However, automatically separating the crowns from each other in aerial imagery is challenging due to factors such as the texture and partial tree crown overlaps. In this study, we present a method to train deep learning models that segment and separate individual trees from RGB and multispectral images, using pseudo-labels derived from aerial laser scanning (ALS) data. Our study shows that the ALS-derived pseudo-labels can be enhanced using a zero-shot instance segmentation model, Segment Anything Model 2 (SAM 2). Our method offers a way to obtain domain-specific training annotations for optical image-based models without any manual annotation cost, leading to segmentation models which outperform any available models which have been targeted for general domain deployment on the same task.

基於增強型激光雷達偽標籤的圖像樹冠分割學習方法

Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels

摘要

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