288 citations · 337 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
A novel statistical metric learning for hyperspectral image classification
Zhiqiang Gong, Ping Zhong, Weidong Hu +2
In this paper, a novel statistical metric learning is developed for spectral-spatial classification of the hyperspectral image. First, the standard variance of the samples of each…
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation
Zhiqiang Gong, Ping Zhong, Weidong Hu +2
This work develops a novel end-to-end deep unsupervised learning method based on convolutional neural network (CNN) with pseudo-classes for remote sensing scene representation. Fir…
Diversity in Machine Learning
Zhiqiang Gong, Ping Zhong, Weidong Hu
Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for specia…