Michele Dalponte

Environmental Engineering · 2008–2015

A researcher in the Science corpus: 4 papers; trusts 9 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 24 citations into it; #1,801 of 5,479 by trust here, #1,430 by citations
• Ecology — 13 citations into it; #3,052 of 10,717 by trust here, #4,523 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Media Technology — 8 citations into it; #1,555 of 3,621 by trust here, #2,033 by citations
• Atmospheric Science — 5 citations into it
• Ecological Modeling — 5 citations into it

Whom Michele Dalponte trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Vladimir Naumovich Vapnik
2. Lorenzo Bruzzone
3. Tomislav Hengl
4. Farid Melgani
5. Gustau Camps-Valls
6. Matthew L. Clark
7. Jonathan Cheung-Wai Chan
8. Hans Ole Ørka
9. Johan Holmgren
10. Anil K. Jain

Who trusts Michele Dalponte overall

1. Michael Alonzo
2. David S. Schimel
3. Markus Immitzer
4. Edwin Raczko
5. Bodo Bookhagen
6. Zhen Zhen
7. Björn Waske
8. Tetsuji Ota
9. Dar A. Roberts
10. Gregory P. Asner

Papers from Michele Dalponte's perspective

Ordered by how strongly the researchers Michele Dalponte trusts endorse each paper. Same corpus, a different order for every researcher.

1. Random Forests
2. Statistical learning theory
3. Classification of hyperspectral remote sensing images with support vector machines (IEEE Transactions on Geoscience and Remote Sensing)
4. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales
5. A Coefficient of Agreement for Nominal Scales
6. A new search algorithm for feature selection in hyperspectral remote sensing images (IEEE Transactions on Geoscience and Remote Sensing)
7. Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions
8. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method
9. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
10. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners (IEEE Transactions on Geoscience and Remote Sensing)

Their papers

• A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space (2015) — 226 citations
• Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data (2014) — 276 citations
• Tree Species Classification in Boreal Forests With Hyperspectral Data (2013) — 340 citations
• Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas (2008) — 561 citations