A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing

By Yinghai Ke, Lindi J. Quackenbush. 2011

354 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/777acba56690b1ba6b00e9aee9fbadb3ed249fef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Lianjun Zhang
2. Zhen Zhen
3. Lindi J. Quackenbush
4. Haohuan Fu
5. Weijia Li
6. Le Yu
7. Arthur Philip Cracknell
8. Juha Hyyppä

In Environmental Engineering

1. Lianjun Zhang
2. Zhen Zhen
3. Lindi J. Quackenbush
4. Haohuan Fu
5. Weijia Li
6. Le Yu
7. Arthur Philip Cracknell
8. Juha Hyyppä

In Nature and Landscape Conservation

1. Zhen Zhen
2. Lianjun Zhang
3. Lindi J. Quackenbush
4. Haohuan Fu
5. Weijia Li
6. Arthur Philip Cracknell
7. Le Yu
8. Juha Hyyppä

Citations in the corpus

• Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data (2016, methodology): “In the image domain, classic treetop detection algorithms include local maximum filtering, image binarization or thresholding, and template matching, while classic crown delineation algorithms include valley-following, region-growing, and watershed segmentation [15].”
• Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images (2016, methodology): “[11] reviewed various methods for automatic individual tree-crown detection and delineation from passive remote sensing, including local maximum filtering, image binarization, scale analysis, and template matching, etc.”
• Remote sensing methods for power line corridor surveys (2016, background): “In general, several studies have been conducted on the extraction of trees from aerial images in forested areas (see a review of methods in Ke and Quackenbush, 2011).”