Hyperspectral Image Classification With Deep Learning Models
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...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2018-09, Vol.56 (9), p.5408-5423 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Yang, Xiaofei Ye, Yunming Li, Xutao Lau, Raymond Y. K. Zhang, Xiaofeng Huang, Xiaohui |
| description | 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. |
| doi_str_mv | 10.1109/TGRS.2018.2815613 |
| format | Article |
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K. ; Zhang, Xiaofeng ; Huang, Xiaohui</creator><creatorcontrib>Yang, Xiaofei ; Ye, Yunming ; Li, Xutao ; Lau, Raymond Y. K. ; Zhang, Xiaofeng ; Huang, Xiaohui</creatorcontrib><description>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. 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K.</creatorcontrib><creatorcontrib>Zhang, Xiaofeng</creatorcontrib><creatorcontrib>Huang, Xiaohui</creatorcontrib><title>Hyperspectral Image Classification With Deep Learning Models</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>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. 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| ispartof | IEEE transactions on geoscience and remote sensing, 2018-09, Vol.56 (9), p.5408-5423 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Artificial neural networks Classification Computer vision Context modeling Convolution Convolutional neural network (CNN) Deep learning Evaluation hyperspectral image Hyperspectral imaging Image classification Image contrast Image enhancement Kernel Machine learning Neural networks Spectral correlation Task analysis Three dimensional models Two dimensional models |
| title | Hyperspectral Image Classification With Deep Learning Models |
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