26 citations · 61 across the 5 of their papers we have counts for
5 papers
DML-GANR: Deep Metric Learning With Generative Adversarial Network Regularization for High Spatial Resolution Remote Sensing Image Retrieval
Yun Cao, Yuebin Wang, Junhuan Peng +4
With a small number of labeled samples for training, it can save considerable manpower and material resources, especially when the amount of high spatial resolution remote sensing…
SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification
Yun Cao, Jie Mei, Yuebin Wang +5
Subspace learning (SL) plays an important role in hyperspectral image (HSI) classification, since it can provide an effective solution to reduce the redundant information in the im…
MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding
Xiaoman Qi, PanPan Zhu, Yuebin Wang +7
To better understand scene images in the field of remote sensing, multi-label annotation of scene images is necessary. Moreover, to enhance the performance of deep learning models…
3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks
Rongtian Ye, Fangyu Liu, Liqiang Zhang
Standard 3D convolution operations require much larger amounts of memory and computation cost than 2D convolution operations. The fact has hindered the development of deep neural n…
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds
Fangyu Liu, Shuaipeng Li, Liqiang Zhang +4
Semantic parsing of large-scale 3D point clouds is an important research topic in computer vision and remote sensing fields. Most existing approaches utilize hand-crafted features…