Hyperspectral Image Classification With Deep Learning Models
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 5408-5423 |
Journal / Publication | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 56 |
Issue number | 9 |
Online published | 17 Apr 2018 |
Publication status | Published - Sep 2018 |
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Abstract
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.
Research Area(s)
- Context modeling, Convolution, Convolutional neural network (CNN), deep learning, hyperspectral image, Hyperspectral imaging, Kernel, Machine learning, Task analysis
Citation Format(s)
Hyperspectral Image Classification With Deep Learning Models. / Yang, Xiaofei; Ye, Yunming; Li, Xutao; Lau, Raymond Y. K.; Zhang, Xiaofeng; Huang, Xiaohui.
In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 9, 09.2018, p. 5408-5423.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review