The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Hyperspectral Image Superresolution by Transfer Learning
Open accessAbstract
Hyperspectral image superresolution is a highly attractive topic in computer vision and has attracted many researchers' attention. However, nearly all the existing methods assume that multiple observations of the same scene are required with the observed low-resolution hyperspectral image. This limits the application of superresolution. In this paper, we propose a new framework to enhance the resolution of hyperspectral images by exploiting the knowledge from natural images: The relationship between low/high-resolution images is the same as that between low/high-resolution hyperspectral images. In the proposed framework, the mapping between low- and high-resolution images can be learned by deep convolutional neural network and be transferred to hyperspectral image by borrowing the idea of transfer learning. In addition, to study the spectral characteristic between low- and high-resolution hyperspectral image, collaborative nonnegative matrix factorization (CNMF) is proposed to enforce collaborations between the low- and high-resolution hyperspectral images, which encourages the estimated solution to extract the same endmembers with low-resolution hyperspectral image. The experimental results on ground based and remote sensing data suggest that the proposed method achieves comparable performance without requiring any auxiliary images of the same scene.
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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
(2010) · International Conference on Machine Learning · Cited by 13,995
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Learning a Deep Convolutional Network for Image Super-Resolution
Dong, C., Loy, C. C., He, K., & Tang, X. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10593-2_13 · Cited by 5,494
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
(1989) · neural information processing systems · Cited by 3,736
On Single Image Scale-Up Using Sparse-Representations
Zeyde, R., Elad, M., & Protter, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-27413-8_47 · Cited by 3,352
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Cited by
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
“lized the potential of applying deep learning techniques for remote sensing tasks such as scene classification [34–38], object detection [39–41], semantic segmentation [42,43], image super-resolution [44] and image retrieval [45–49]. For RSIR, the deep learning techniques can be roughly divided into unsupervised featu…”
Background
Metadata
- Authors
- Yuan, Y., Zheng, X., & Lu, X.
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2017-05
- Volume
- 10
- Issue
- 5
- Pages
- 1963-1974
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2588000623
- Cited by
- 267
- References
- 54
- FWCI
- 21.93
- Type
- journal article
- Language
- en
- Open access
- green
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- Yuan, Y., Zheng, X., & Lu, X. (2017). Hyperspectral Image Superresolution by Transfer Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2017.2655112
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