Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods

By Juho Pitkänen. 2001

114 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/c704422f0403c39229cca1354e8a4c79e8946644

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. Lindi J. Quackenbush
2. J. V. van Aardt
3. Fethi B. Ahmed
4. F. Blakeway
5. Yinghai Ke
6. Yann Nouvellon
7. Michael Gebreslasie
8. Guerric le Maire

In Environmental Engineering

1. Lindi J. Quackenbush
2. J. V. van Aardt
3. Fethi B. Ahmed
4. Pierre Couteron
5. F. Blakeway
6. Yinghai Ke
7. Michael Gebreslasie
8. Lianjun Zhang

In Ocean Engineering

1. J. V. van Aardt
2. Fethi B. Ahmed
3. F. Blakeway
4. Yinghai Ke
5. Michael Gebreslasie
6. Lindi J. Quackenbush

Citations in the corpus

• A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing (2011, methodology): “Pitka¨nen (2001) and Pouliot et al. (2005) considered a detected treetop within a certain distance from the reference position as accurate detection.”
• Individual tree detection based on variable and fixed window size local maxima filtering applied to IKONOS imagery for even-aged Eucalyptus plantation forests (2011, methodology): “This finding compared favourably with the previously published research by Wulder et al. (2000) and Pitkänen (2001).”
• Mapping local density of young Eucalyptus plantations by individual tree detection in high spatial resolution satellite images (2013, background): “Finally, and as done in several other studies (e.g. Pitkänen, 2001; Pouliot et al., 2005) we used an overall accuracy index, noted AI , to measure the performance of any given set of parameters: This index corrects the validated rate by taking account both of omission and commission errors.”