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Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks

  • Published:2021 Issue: Volume:14 Page:11669-11682
  • ISSN:1939-1404
  • Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • language:
  • Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wan Lulu,Chen TaoORCID,Plaza AntonioORCID,Cai Haojie

Funder

National Natural Science Foundation of China

Opening Foundation of Qilian Mountain National Park Research Center

Opening Foundation of Beijing Key Laboratory of Urban Spatial Information Engineering

Opening Foundation of Geomatics Technology and Application Key Laboratory of Qinghai Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Cited by 12 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. DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

2. Beyond Pixel-Wise Unmixing: Spatial–Spectral Attention Fully Convolutional Networks for Abundance Estimation;Remote Sensing;2023-12-12

3. Proportion Inference Using Deep Neural Networks. Applications to X-Ray Diffraction and Hyperspectral Imaging;2023 31st European Signal Processing Conference (EUSIPCO);2023-09-04

4. A Dual-Stream Convolutional Feature Fusion Network for Hyperspectral Unmixing;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

5. Spatial Validation of Spectral Unmixing Results: A Systematic Review;Remote Sensing;2023-05-29
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