Powered by the advances of optical remote sensing sensors, the production of
very high spatial resolution multispectral images provides great potential for
achieving cost-efficient and high-accuracy forest inventory and analysis in an
automated way. Lots of studies that aim at providing an inventory to the level
of each individual tree have generated a variety of methods for Individual Tree
Crown Detection and Delineation (ITCD). This review covers ITCD methods for
detecting and delineating individual tree crowns, and systematically reviews
the past and present of ITCD-related researches applied to the optical remote
sensing images. With the goal to provide a clear knowledge map of existing ITCD
efforts, we conduct a comprehensive review of recent ITCD papers to build a
meta-data analysis, including the algorithm, the study site, the tree species,
the sensor type, the evaluation method, etc. We categorize the reviewed methods
into three classes: (1) traditional image processing methods (such as local
maximum filtering, image segmentation, etc.); (2) traditional machine learning
methods (such as random forest, decision tree, etc.); and (3) deep learning
based methods. With the deep learning-oriented approaches contributing a
majority of the papers, we further discuss the deep learning-based methods as
semantic segmentation and object detection methods. In addition, we discuss
four ITCD-related issues to further comprehend the ITCD domain using optical
remote sensing data, such as comparisons between multi-sensor based data and
optical data in ITCD domain, comparisons among different algorithms and
different ITCD tasks, etc. Finally, this review proposes some ITCD-related
applications and a few exciting prospects and potential hot topics in future
ITCD research.

基于光学遥感传感器的先进技术，高空间分辨率多光谱图像的制作为实现经济高精度的森林清查和分析提供了巨大潜力。本综述旨在对光学遥感图像进行个体树冠检测和描绘相关研究进行系统回顾，并将所评估的方法归类为传统图像处理方法、传统机器学习方法和基于深度学习的方法。同时，讨论了与光学遥感数据相关的若干问题和未来的研究方向。