2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-6.3
Paper Title HYPERSPECTRAL IMAGE SUPER-RESOLUTION VIA ADJACENT SPECTRAL FUSION STRATEGY
Authors Qiang Li, Qi Wang, Xuelong Li, Northwestern Polytechnical University, China
SessionIVMSP-6: Super-resolution 2 & Multi-scale Processing
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Hyperspectral image exhibits low spatial resolution due to the limitation of imaging system. Improving it without an auxiliary high resolution (HR) image still remains a challenging problem. Recently, although many deep learning-based hyperspectral image super-resolution (SR) methods have been proposed, they make the insufficient utilization of adjacent bands to improve the reconstruction performance. To address this issue, we explore a new structure for hyperspectral image SR via adjacent spectral fusion strategy. Inspired by the high similarity among adjacent bands, neighboring band partition is proposed to divide the adjacent bands into several groups. Through the current band, the adjacent bands is guided to enhance the exploration ability. To explore more complementary information, an alternative fusion mechanism, i.e., intra-group fusion and inter-group fusion, is designed, which helps to recover the missing details in the current band. Experiments demonstrate that our approach produces the state-of-the-art results over the existing approaches.