Jointly Learning Band Selection and Filter Array Design for Hyperspectral Imaging-Reference-Cited by-同舟云学术

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Jointly Learning Band Selection and Filter Array Design for Hyperspectral Imaging

Published:2023-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
language:
Short-container-title:

Author:

Li Ke¹,Dai Dengxin²,Van Gool Luc¹

Affiliation:

1. CVL, ETH Zurich

2. MPI for Informatics

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10030081/10030084/10030979.pdf?arnumber=10030979

Reference68 articles.

1. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

2. A new search algorithm for feature selection in hyperspectral remote sensing images

3. Multi-Spectral Imaging by Optimized Wide Band Illumination

4. Opti-MSFA: a toolbox for generalized design and optimization of multispectral filter arrays

5. Mosaic Convolution-Attention Network for Demosaicing Multispectral Filter Array Images

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep jointly optical spectral band selection and classification learning;Applied Optics;2024-07-11

2. Learning a physics-based filter attachment for hyperspectral imaging with RGB cameras;Neurocomputing;2024-05

3. InSPECtor: an end-to-end design framework for compressive pixelated hyperspectral instruments;Applied Optics;2023-09-13

4. LiteCCLKNet: A lightweight criss‐cross large kernel convolutional neural network for hyperspectral image classification;IET Computer Vision;2023-07-24

5. PPI Edge Infused Spatial–Spectral Adaptive Residual Network for Multispectral Filter Array Image Demosaicing;IEEE Transactions on Geoscience and Remote Sensing;2023

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