6 papers
Hyperspectral Calibration Detection: A Novel Concept For Change Detection With Unsupervised Incremental Safe Pseudo-Labeling Implementation
Chia-Hsiang Lin, Shih-Min Hsu, Ching-Yun Liang +2
Hyperspectral change detection (HCD) has found numerous key applications, such as land cover monitoring. The majority of benchmark HCD algorithms are semi-supervised methods, and s…
Hyperspectral Anomaly Detection Using Einstein Fuzzy Computing and Quantum Neural Network
Chia-Hsiang Lin, Si-Sheng Young, Reza Langari
In the remote sensing (RS) field, hyperspectral imagery provides rich spectral information and facilitates numerous critical applications, such as material identification. Among th…
Deep Unfolding Real-Time Super-Resolution Using Subpixel-Shift Twin Image and Convex Self-Similarity Prior
Chia-Hsiang Lin, Wei-Chih Liu, Yu-En Chiu +1
Multi-image super-resolution (MISR) is a critical technique for satellite remote sensing. In the perspective of information, twin-image super-resolution (TISR) is regarded as the m…
COS2A: Conversion from Sentinel-2 to AVIRIS Hyperspectral Data Using Interpretable Algorithm With Spectral-Spatial Duality
Chia-Hsiang Lin, Jui-Ting Chen, Zi-Chao Leng +1
The Sentinel-2 satellite, launched by the European Space Agency (ESA), offers extensive spatial coverage and has become indispensable in a wide range of remote sensing applications…
Quantum-Driven Multihead Inland Waterbody Detection With Transformer-Encoded CYGNSS Delay-Doppler Map Data
Chia-Hsiang Lin, Jhao-Ting Lin, Po-Ying Chiu +2
Inland waterbody detection (IWD) is critical for water resources management and agricultural planning. However, the development of high-fidelity IWD mapping technology remains unre…
PRIME: Blind Multispectral Unmixing Using Virtual Quantum Prism and Convex Geometry
Chia-Hsiang Lin, Jhao-Ting Lin
Multispectral unmixing (MU) is critical due to the inevitable mixed pixel phenomenon caused by the limited spatial resolution of typical multispectral images in remote sensing. How…