Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures

By Mats Erikson. 2004

116 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/b62184cc147e8179a39edda9cb9df7bce8148e67

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Christian Ginzler
2. Emmanuel Baltsavias
3. Lorenz Hurni
4. Meinrad Kuechler
5. Lars T. Waser
6. N. E. Zimmermann

In Environmental Engineering

1. Christian Ginzler
2. Emmanuel Baltsavias
3. Lorenz Hurni
4. Meinrad Kuechler
5. Lars T. Waser
6. N. E. Zimmermann
7. Achilleas Psomas
8. Xiangguo Lin

Citations in the corpus

• Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013, background): “Over the last decade, high spatial resolution data (pixel size of one meter or less) became increasingly available, opening a new round of research on classifying tree species at the individual tree level (Brandtberg, 2002; Key et al., 2001; Erikson, 2004).”
• Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data (2011, methodology): “E.g. Erikson (2004) obtained an overall accuracy of 77% and Brandtberg (2002) between 67% and 79%.”