Integrating Traditional and Deep Learning Methods to Detect Tree Crowns in Satellite Images

2025-07-02

AI Summary

To address insufficient automation in forest monitoring amid global warming and biodiversity loss, this paper proposes a remote sensing-based tree crown detection method integrating classical image processing with deep learning. First, multiscale segmentation and morphological feature extraction segment forested areas; then, a lightweight CNN localizes candidate tree crowns; finally, a post-processing engine—guided by proximity constraints and local consistency rules—refines detection outputs. The key contribution is an interpretable, rule-driven fusion strategy that effectively bridges the robustness of traditional methods and the discriminative power of deep learning. Experiments on Sentinel-2 imagery demonstrate significant improvements: a 12.7% increase in crown recall and a mean average precision at IoU=0.5 (mAP@0.5) of 86.4%. These results validate the feasibility and superiority of synergistic multimodal approaches for large-scale, dynamic forest monitoring.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• Detect tree crowns in satellite images automatically
• Integrate traditional and deep learning for better accuracy
• Improve forest monitoring to address environmental issues

Innovation

• Integrates traditional and deep learning methods
• Uses rule-based approach for post-processing
• Enhances tree crown detection robustness