Xin Huang

Computer Vision and Pattern Recognition · IEEE Transactions on Geoscience and Remote Sensing · 2009–2015

A researcher in the Science corpus: 10 papers; trusts 192 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 97 citations into it; #6,640 of 21,575 by trust here, #5,754 by citations
• Media Technology — 77 citations into it; #448 of 3,621 by trust here, #339 by citations
• Atmospheric Science — 41 citations into it; #554 of 4,804 by trust here, #987 by citations
• Artificial Intelligence — 34 citations into it
• Representation & Self-Supervised Learning — 30 citations into it; #1,952 of 3,145 by trust here, #1,943 by citations
• Ecology — 23 citations into it; #2,047 of 10,717 by trust here, #5,318 by citations
• Analytical Chemistry — 19 citations into it
• Signal Processing — 18 citations into it

Whom Xin Huang trusts overall

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

1. Jon Atli Benediktsson
2. Martino Pesaresi
3. Björn Waske
4. Thomas Blaschke
5. Lorenzo Bruzzone
6. Mathieu Fauvel
7. Aapo Hyvärinen
8. Giles M. Foody
9. J. Platt
10. Aixia Guo

Who trusts Xin Huang overall

1. E. Aptoula
2. Junwei Han
3. Wei Li
4. Gong Cheng
5. Liangpei Zhang
6. Mercedes Eugenia Paoletti
7. Lefei Zhang
8. Wenzhi Zhao
9. Xudong Kang
10. Bo Du

Papers from Xin Huang's perspective

Ordered by how strongly the researchers Xin Huang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Going deeper with convolutions
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Multiple Feature Learning for Hyperspectral Image Classification (2015) — 302 citations
• Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification (2015) — 163 citations
• Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding (2015) — 302 citations
• Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction (2013) — 308 citations
• Hyperspectral image noise reduction based on rank-1 tensor decomposition (2013) — 112 citations
• An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery (2013) — 441 citations
• Morphological Building/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas (2012) — 367 citations
• On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification (2012) — 406 citations
• Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance (2011) — 112 citations
• Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery (2009) — 127 citations