融合传统与深度学习方法，提升卫星图像中树冠检测的准确率和鲁棒性。
Integrating Traditional and Deep Learning Methods to Detect Tree Crowns in Satellite Images
- 用传统方法提取特征并分割林区，深度学习识别树冠。
- 通过规则后处理，结合邻近树木信息提升检测数量。
- 适合需要高精度森林监测的生态研究与环保应用。
全球变暖、生物多样性丧失和空气污染是地球面临的主要问题之一。其中一个重要挑战是缺乏对森林的有效监测以保护它们。为应对这一问题，有必要利用遥感与计算机视觉技术实现监测自动化。因此，基于传统方法和深度学习的自动树冠检测算法应运而生。本研究首先提出两种基于不同方法的树冠检测方案，随后构建一种新型基于规则的集成方法，以提升检测结果的鲁棒性和准确性。传统方法用于特征提取与林区分割，深度学习则用于树冠检测。通过所提出的规则后处理策略，结合邻近树木信息与局部操作，旨在增加检测到的树冠数量。我们对比了该方法在检测树冠数量方面的表现，并分析了其优缺点及改进方向。
原文摘要 · Abstract (English)
Global warming, loss of biodiversity, and air pollution are among the most significant problems facing Earth. One of the primary challenges in addressing these issues is the lack of monitoring forests to protect them. To tackle this problem, it is important to leverage remote sensing and computer vision methods to automate monitoring applications. Hence, automatic tree crown detection algorithms emerged based on traditional and deep learning methods. In this study, we first introduce two different tree crown detection methods based on these approaches. Then, we form a novel rule-based approach that integrates these two methods to enhance robustness and accuracy of tree crown detection results. While traditional methods are employed for feature extraction and segmentation of forested areas, deep learning methods are used to detect tree crowns in our method. With the proposed rule-based approach, we post-process these results, aiming to increase the number of detected tree crowns through neighboring trees and localized operations. We compare the obtained results with the proposed method in terms of the number of detected tree crowns and report the advantages, disadvantages, and areas for improvement of the obtained outcomes.
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