activity
20182022
most citedMulti-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

12 citations · 36 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

10 papers · 1 filter

cs.CV2022

Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders

Jiahao Qi, Zhiqiang Gong, Xingyue Liu +6

Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversaria…

cs.CV2022★ 11 cited

A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification

Zhiqiang Gong, Ping Zhong, Jiahao Qi +1

Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic…

cs.CV2022

Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification

Aihuan Yao, Jiahao Qi, Ping Zhong

Compared with existing vehicle re-identification (ReID) tasks conducted with datasets collected by fixed surveillance cameras, vehicle ReID for unmanned aerial vehicle (UAV) is sti…

cs.CV2020★ 2 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…