7 papers
Anti-Hyperspectral Anomaly Detection: A First Study on Stealthy Lipschitz-Forcing Perturbations Against Unknown Detectors
Chia-Hsiang Lin, Si-Sheng Young, Jon Atli Benediktsson
Hyperspectral imagery represents the best contemporary technology to remotely detect anomalous objects. Nevertheless, hyperspectral anomaly detection (HAD) technique makes ground f…
Underdetermined Blind Source Separation via Weighted Simplex Shrinkage Regularization and Quantum Deep Image Prior
Chia-Hsiang Lin, Si-Sheng Young
As most optical satellites remotely acquire multispectral images (MSIs) with limited spatial resolution, multispectral unmixing (MU) becomes a critical signal processing technology…
Spectral Super-Resolution via Adversarial Unfolding and Data-Driven Spectrum Regularization: From Multispectral Satellite Data to NASA Hyperspectral Image
Si-Sheng Young, Chia-Hsiang Lin
The European Space Agency's Sentinel-2 satellite provides global multispectral coverage for remote sensing (RS) applications. However, limited spectral resolution (12 bands) and no…
A Quantum-Empowered SPEI Drought Forecasting Algorithm Using Spatially-Aware Mamba Network
Po-Wei Tang, Chia-Hsiang Lin, Jian-Kai Huang +1
Due to the intensifying impacts of extreme climate changes, drought forecasting (DF), which aims to predict droughts from historical meteorological data, has become increasingly cr…
HyperKING: Quantum-Classical Generative Adversarial Networks for Hyperspectral Image Restoration
Chia-Hsiang Lin, Si-Sheng Young
Quantum machine intelligence starts showing its impact on satellite remote sensing (SRS). Also, recent literature exhibits that quantum generative intelligences encompass superior…
Quantum Feature-Empowered Deep Classification for Fast Mangrove Mapping
Chia-Hsiang Lin, Po-Wei Tang, Alfredo R. Huete
A mangrove mapping (MM) algorithm is an essential classification tool for environmental monitoring. The recent literature shows that compared with other index-based MM methods that…