Peter M. Atkinson

Media Technology · Isprs Journal of Photogrammetry and Remote Sensing · 1998–2021

A researcher in the Science corpus: 15 papers; trusts 142 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Media Technology — 127 citations into it; #853 of 3,621 by trust here, #575 by citations
• Computer Vision and Pattern Recognition — 125 citations into it
• Ecology — 67 citations into it; #5,746 of 10,717 by trust here, #4,126 by citations
• Artificial Intelligence — 61 citations into it; #12,315 of 35,345 by trust here, #23,731 by citations
• Atmospheric Science — 39 citations into it
• Environmental Engineering — 39 citations into it; #1,232 of 5,479 by trust here, #2,025 by citations
• Global and Planetary Change — 30 citations into it
• Ecological Modeling — 8 citations into it

Whom Peter M. Atkinson trusts overall

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

1. Paul H. C. Eilers
2. Per Jönsson
3. Jennifer N. Hird
4. Bo Huang
5. Feng Gao
6. C. Parmesan
7. Khaled Hazaymeh
8. Lars Eklundh
9. Mahesh Pal
10. Kaiming He

Who trusts Peter M. Atkinson overall

1. Saro Lee
2. Zhu Lei
3. Josée Lévesque
4. Biswajeet Pradhan
5. Arzu Erener
6. Ramón Giraldo
7. Kian Pang Tan
8. Ahmed M. Youssef
9. Gang Chen
10. Miao Li

Papers from Peter M. Atkinson's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. Going deeper with convolutions
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery (2021) — 1,269 citations
• COVID-19 Outbreak Prediction with Machine Learning (2020) — 438 citations
• Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019) — 393 citations
• Joint Deep Learning for land cover and land use classification (2019) — 458 citations
• Spatio-temporal fusion for daily Sentinel-2 images (2018) — 351 citations
• An object-based convolutional neural network (OCNN) for urban land use classification (2018) — 423 citations
• A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification (2017) — 391 citations
• Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology (2012) — 491 citations
• Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture (2012) — 565 citations
• Spatial analysis for epidemiology. (2004) — 411 citations
• Uncertainty in remote sensing and GIS (2002) — 229 citations
• Assessing uncertainty in estimates with ordinary and indicator kriging (2001) — 109 citations
• Super-resolution target identification from remotely sensed images using a Hopfield neural network (2001) — 336 citations
• Geostatistical classification for remote sensing: an introduction (2000) — 306 citations
• Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy (1998)