Quanlong Feng

Ecology · 2015

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Ecology — 22 citations into it; #5,375 of 10,717 by trust here, #9,173 by citations
• Environmental Engineering — 13 citations into it; #2,274 of 5,479 by trust here, #3,203 by citations
• Media Technology — 12 citations into it; #2,220 of 3,621 by trust here, #2,820 by citations
• Global and Planetary Change — 8 citations into it; #6,970 of 10,225 by trust here, #8,787 by citations
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Quanlong Feng trusts overall

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

1. Victor Francisco Rodriguez-Galiano
2. Robert Martin Haralick
3. Giorgos Mountrakis
4. Mario Chica-Olmo
5. Ismael Colomina
6. Andrea S. Laliberte
7. Jungho Im
8. Jiantao Liu
9. Dengsheng Lu
10. Xiaoxiao Li

Who trusts Quanlong Feng overall

1. Milan Erdelj
2. H. Yao
3. Mingyang Lyu
4. Michal Król
5. Rongjun Qin
6. Jiantao Liu
7. Yibo Zhao
8. Phan Thanh Noi
9. Farzaneh Sajedi Hosseini

Papers from Quanlong Feng's perspective

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

1. Textural Features for Image Classification
2. Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery
3. Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography (Computers, Environment and Urban Systems)
4. Support vector machines in remote sensing: A review
5. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis (Remote Sensing)
6. An assessment of the effectiveness of a random forest classifier for land-cover classification
7. A survey of image classification methods and techniques for improving classification performance
8. Object-based urban vegetation mapping with high-resolution aerial photography as a single data source
9. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery
10. Random forest classifier for remote sensing classification

Their papers

• Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China (2015) — 306 citations
• UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis (2015) — 552 citations