288 citations · 304 across the 9 of their papers we have counts for
9 papers
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
Zhiqiang Gong, Xian Zhou, Wen Yao +2
The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, pro…
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…
Transferable Physical Attack against Object Detection with Separable Attention
Yu Zhang, Zhiqiang Gong, Yichuang Zhang +5
Transferable adversarial attack is always in the spotlight since deep learning models have been demonstrated to be vulnerable to adversarial samples. However, existing physical att…
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…
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…