中文标题#
基於機載激光雷達點雲的樹實例分割的弱監督學習
英文标题#
Weakly-Supervised Learning for Tree Instances Segmentation in Airborne Lidar Point Clouds
中文摘要#
機載激光掃描（ALS）數據的樹木實例分割對於森林監測至關重要，但由於傳感器分辨率、採集時植被狀態、地形特徵等因素導致的數據變化，仍然具有挑戰性。 此外，獲取足夠精確標記的數據來訓練完全監督的實例分割方法成本很高。 為了解決這些挑戰，我們提出了一種弱監督方法，其中通過人工操作員提供的初始分割結果的標籤作為質量評分，該初始分割結果可以通過非微調模型或閉式算法獲得。 在質量評估過程中生成的標籤隨後用於訓練一個評分模型，其任務是將分割輸出分類為與人工操作員指定的相同類別。 最後，使用評分模型的反饋對分割模型進行微調。 這反過來使原始分割模型在正確識別的樹木實例方面提高了 34%，同時顯著減少了預測的非樹木實例數量。 在以小樹（高度小於兩米）或包含灌木、岩石等複雜環境為特徵的稀疏森林區域，仍然存在挑戰，這些區域可能會被誤認為樹木，導致所提出方法的性能下降。
英文摘要#
Tree instance segmentation of airborne laser scanning (ALS) data is of utmost importance for forest monitoring, but remains challenging due to variations in the data caused by factors such as sensor resolution, vegetation state at acquisition time, terrain characteristics, etc. Moreover, obtaining a sufficient amount of precisely labeled data to train fully supervised instance segmentation methods is expensive. To address these challenges, we propose a weakly supervised approach where labels of an initial segmentation result obtained either by a non-finetuned model or a closed form algorithm are provided as a quality rating by a human operator. The labels produced during the quality assessment are then used to train a rating model, whose task is to classify a segmentation output into the same classes as specified by the human operator. Finally, the segmentation model is finetuned using feedback from the rating model. This in turn improves the original segmentation model by 34% in terms of correctly identified tree instances while considerably reducing the number of non-tree instances predicted. Challenges still remain in data over sparsely forested regions characterized by small trees (less than two meters in height) or within complex surroundings containing shrubs, boulders, etc. which can be confused as trees where the performance of the proposed method is reduced.
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