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

TL;DR

This paper presents a novel rule-based approach that combines traditional image processing and deep learning techniques to improve the accuracy of tree crown detection in satellite images for forest monitoring.

Contribution

It introduces an integrated method that leverages both traditional and deep learning techniques, enhancing robustness and detection accuracy in satellite-based forest monitoring.

Findings

Improved detection accuracy over individual methods

Enhanced robustness through rule-based post-processing

Identified areas for further method refinement

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.