Hyperspectral Image Classification With Deep Learning Models

By Xiaofei Yang, Yunming Ye, Xutao Li, Raymond Y. K. Lau, Xiaofeng Zhang, Xiaohui Huang. IEEE Transactions on Geoscience and Remote Sensing, 2018

435 citations recorded by the source corpus.

Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c522d0ecba069ab0e903e93af1bb89c54daed796

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Shutao Li
2. Leyuan Fang
3. Jon Atli Benediktsson
4. Pedram Ghamisi
5. Jocelyn Chanussot
6. Swalpa Kumar Roy
7. Danfeng Hong
8. Yushi Chen

In Computer Vision and Pattern Recognition

1. Leyuan Fang
2. Jocelyn Chanussot
3. Shutao Li
4. Pedram Ghamisi
5. Danfeng Hong
6. Jon Atli Benediktsson
7. Yushi Chen
8. Swalpa Kumar Roy

In Media Technology

1. Shutao Li
2. Leyuan Fang
3. Jocelyn Chanussot
4. Pedram Ghamisi
5. Jon Atli Benediktsson
6. Danfeng Hong
7. Yushi Chen
8. Swalpa Kumar Roy

Citations in the corpus

• Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification (2019, neutral): “Table VIII reports the running time of deep models including GCN, S 2 GCN, R-2D-CNN, DR-CNN, and the proposed MDGCN on the three datasets adopted above.”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “CNN and recurrent 3D-CNN for HSIC was proposed in [205] and along with an interesting comparison of these frameworks with their corresponding 2D and 3D-CNN models, which validates the superiority of recurrent CNN.”
• Deep Learning for Hyperspectral Image Classification: An Overview (2019, methodology): “Based on this fact, 3-D CNN was used to effectively extract deep spectral–spatial-combined features for accurate HSI classification without relying on any preprocessing or postprocessing techniques [56]–[58], [92]–[95].”