activity
20182020
most citedMulti-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

12 citations · 25 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CV20202 cited

Few-shot Object Detection with Self-adaptive Attention Network for Remote Sensing Images

Zixuan Xiao, Wei Xue, Ping Zhong

In remote sensing field, there are many applications of object detection in recent years, which demands a great number of labeled data. However, we may be faced with some cases whe…

cs.CV2020

Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images

Zixuan Xiao, Ping Zhong, Yuan Quan +2

In recent years, there are many applications of object detection in remote sensing field, which demands a great number of labeled data. However, in many cases, data is extremely ra…

cs.CV2019

Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification

Zhiqiang Gong, Ping Zhong, Weidong Hu

Nowadays, deep learning methods, especially the convolutional neural networks (CNNs), have shown impressive performance on extracting abstract and high-level features from the hype…

cs.CV2019

Deep Manifold Embedding for Hyperspectral Image Classification

Zhiqiang Gong, Weidong Hu, Xiaoyong Du +2

Deep learning methods have played a more and more important role in hyperspectral image classification. However, the general deep learning methods mainly take advantage of the info…

cs.LG201910 cited

Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network

Sheng Wan, Chen Gong, Ping Zhong +3

In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based metho…

eess.IV201912 cited

Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Sheng Wan, Chen Gong, Ping Zhong +3

Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. How…