14 papers
Defending against Patch-Based and Texture-Based Adversarial Attacks with Spectral Decomposition
Wei Zhang, Xinyu Chang, Xiao Li +2
Adversarial examples present significant challenges to the security of Deep Neural Network (DNN) applications. Specifically, there are patch-based and texture-based attacks that ar…
Unmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification
Yimin Zhu, Lincoln Linlin Xu
Although hyperspectral image (HSI) classification is critical for supporting various environmental applications, it is a challenging task due to the spectral-mixture effect, the sp…
DSCSNet: A Dynamic Sparse Compression Sensing Network for Closely-Spaced Infrared Small Target Unmixing
Zhiyang Tang, Yiming Zhu, Ruimin Huang +4
Due to the limitations of optical lens focal length and detector resolution, distant clustered infrared small targets often appear as mixed spots. The Close Small Object Unmixing (…
mHC-HSI: Clustering-Guided Hyper-Connection Mamba for Hyperspectral Image Classification
Yimin Zhu, Zack Dewis, Quinn Ledingham +6
Recently, DeepSeek has invented the manifold-constrained hyper-connection (mHC) approach which has demonstrated significant improvements over the traditional residual connection in…
Clustering-Guided Spatial-Spectral Mamba for Hyperspectral Image Classification
Zack Dewis, Yimin Zhu, Zhengsen Xu +4
Although Mamba models greatly improve Hyperspectral Image (HSI) classification, they have critical challenges in terms defining efficient and adaptive token sequences for improve p…
White-Box mHC: Electromagnetic Spectrum-Aware and Interpretable Stream Interactions for Hyperspectral Image Classification
Yimin Zhu, Lincoln Linlin Xu, Zhengsen Xu +6
In hyperspectral image classification (HSIC), most deep learning models rely on opaque spectral-spatial feature mixing, limiting their interpretability and hindering understanding…