activity
20182026
most citedGenerative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification

167 citations · 192 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Self-Supervised Tree-level Biomass Estimation in Urban Environments From Airborne LiDAR and Optical Observations

Jose Bermudez, Zilong Zhong, Dominic Cyr +2

Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse products that cannot resolve in…

cs.CV202025 cited

Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

Ying Li, Lingfei Ma, Zilong Zhong +4

Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated…

cs.CV2019

Squeeze-and-Attention Networks for Semantic Segmentation

Zilong Zhong, Zhong Qiu Lin, Rene Bidart +6

The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features. Howeve…

eess.IV2019167 cited

Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification

Zilong Zhong, Jonathan Li, David A. Clausi +1

In this paper, we address the hyperspectral image (HSI) classification task with a generative adversarial network and conditional random field (GAN-CRF) -based framework, which int…

cs.CV2018

Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification

Zilong Zhong, Jonathan Li

High spectral dimensionality and the shortage of annotations make hyperspectral image (HSI) classification a challenging problem. Recent studies suggest that convolutional neural n…