The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
Open accessAbstract
In this article, we propose an efficient and effective framework to fuse hyperspectral and light detection and ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter-sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy (OA) of 96.03%. On the Trento data, it achieves an OA of 99.12%. These results sufficiently certify the effectiveness of our proposed model.
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
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Self-citationChen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2584107 · Cited by 2,984
Self-citationDeep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Zhang, L., Zhang, L., & Du, B. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2540798 · Cited by 2,208
Zhong, Z., Li, J., Luo, Z., & Chapman, M. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2755542 · Cited by 1,959
Cheng, G., Zhou, P., & Han, J. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2601622 · Cited by 1,771
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
Hong, D., Yokoya, N., Chanussot, J., & Zhu, X. X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2878958 · Cited by 948
Self-citationGhamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Self-citationConvolutional neural networks for hyperspectral image classification
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Multisource Remote Sensing Data Classification Based on Convolutional Neural Network
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Convolutional Neural Networks for Multimodal Remote Sensing Data Classification
Wu, X., Hong, D., & Chanussot, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3124913 · Cited by 412
Self-citationHyperspectral Image Classification With Attention-Aided CNNs
Hang, R., Li, Z., Liu, Q., Ghamisi, P., & Bhattacharyya, S. S. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3007921 · Cited by 341
Self-citationA comparative review on multi-modal sensors fusion based on deep learning
Tang, Q., Liang, J., & Zhu, F. (2023) · Signal Processing · doi:10.1016/j.sigpro.2023.109165 · Cited by 280
A Feature Selection Method for Multimodal Multispectral LiDAR Sensing
Han, Y., Salido-Monzú, D., Butt, J. A., Schweizer, S., & Wieser, A. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.04.022 · Cited by 6
Metadata
- Authors
- Hang, R., Li, Z., Ghamisi, P., Hong, D., Xia, G., & Liu, Q.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2020-07
- Volume
- 58
- Issue
- 7
- Pages
- 4939-4950
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W3004968762
- Cited by
- 466
- References
- 35
- FWCI
- 24.84
- Type
- journal article
- Language
- en
- Open access
- green
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