153 citations · 211 across the 6 of their papers we have counts for
6 papers
Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation
Yijia Xu, Liqiang Zhang, Wuming Zhang +5
Road network extraction from satellite images is widely applicated in intelligent traffic management and autonomous driving fields. The high-resolution remote sensing images contai…
Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
Lei Ding, Dong Lin, Shaofu Lin +5
Long-range contextual information is crucial for the semantic segmentation of High-Resolution (HR) Remote Sensing Images (RSIs). However, image cropping operations, commonly used f…
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…
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…