Peng Gong

Computer Vision and Pattern Recognition · Remote Sensing · 2003–2019

A researcher in the Science corpus: 13 papers; trusts 9 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 17 citations into it
• Artificial Intelligence — 14 citations into it; #24,846 of 35,345 by trust here, #28,481 by citations
• Environmental Engineering — 9 citations into it; #1,989 of 5,479 by trust here, #1,260 by citations
• Evaluation & Benchmarks — 7 citations into it
• Statistical Learning Theory — 5 citations into it
• Nature and Landscape Conservation — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Peng Gong trusts overall

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

1. Stéphane Mallat
2. Leo Breiman
3. Jerome H. Friedman
4. José Manuel Peñá-Barragán
5. Chih-Jen Lin
6. Bernard Walter Silverman
7. Victor Francisco Rodriguez-Galiano
8. Jon Atli Benediktsson
9. Martin Herold
10. Ingrid Daubechies

Who trusts Peng Gong overall

1. George Alan Blackburn
2. Lei Ji
3. Francesca Bovolo
4. Martin Schlerf
5. Thomas Blaschke
6. Jingfeng Huang
7. Giles M. Foody
8. Michele Dalponte
9. Urska Kanjir
10. Yuan Yuan

Papers from Peng Gong's perspective

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

1. LIBSVM: A library for support vector machines
2. Neural Networks for Pattern Recognition
3. Experiments with a New Boosting Algorithm
4. A wavelet tour of signal processing
5. Stochastic gradient boosting
6. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
7. AND T
8. Status of land cover classification accuracy assessment
9. Spectral resolution requirements for mapping urban areas (IEEE Transactions on Geoscience and Remote Sensing)
10. Ten Lectures on Wavelets

Their papers

• Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. (2019) — 1,096 citations
• Mapping Urban Land Use by Using Landsat Images and Open Social Data (2016) — 380 citations
• Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution (2015) — 131 citations
• Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery (2014) — 347 citations
• Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery (2014) — 389 citations
• Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives (2012) — 369 citations
• Accuracy Assessment Measures for Object-based Image Segmentation Goodness (2010) — 347 citations
• Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA (2008) — 117 citations
• Detection of individual trees and estimation of tree height using LiDAR data (2007) — 263 citations
• Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery (2006) — 814 citations
• Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping (2004) — 170 citations
• Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data (2003) — 428 citations
• Land-use/land-cover change detection using improved change-vector analysis (2003) — 373 citations