Segmented canonical discriminant analysis of in situ hyperspectral data for identifying 13 urban tree species

By Ruiliang Pu, Desheng Liu. 2011

47 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/301c962089a8ed774afe4d9402255179ac86259f

How researchers see this paper

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

In Analytical Chemistry

1. Bodo Bookhagen
2. Michael Alonzo
3. Keely L. Roth
4. Dar A. Roberts

In Ecology

1. Michael Alonzo
2. Keely L. Roth
3. Bodo Bookhagen
4. Dar A. Roberts
5. Philip E. Dennison

In Media Technology

1. Michael Alonzo
2. Keely L. Roth
3. Bodo Bookhagen
4. Dar A. Roberts
5. Philip E. Dennison

Citations in the corpus

• Identifying Santa Barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis (2013, methodology): “Pu and Liu (2011) used in-situ, leaf-level spectra from 13 urban species in Tampa, Florida, to reach 90% classification accuracy using segmented CDA and 89% accuracy using stepwise discriminant analysis (SDA) versus 80% accuracy for PCA with a linear discriminant analysis (LDA) classifier.”
• Urban tree species mapping using hyperspectral and lidar data fusion (2014, methodology): “Data reductionwith this technique has been successfully applied to remote sensing classification problems including urban tree species discrimination (Alonzo et al., 2013; Pu & Liu, 2011; Zhao & Maclean, 2000).”