TY - JOUR
T1 - Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network
AU - Zhang, Haokui
AU - Li, Ying
AU - Zhang, Yuzhu
AU - Shen, Qiang
N1 - Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2017/5/4
Y1 - 2017/5/4
N2 - In this article, a novel dual-channel convolutional neural network (DC-CNN) framework is proposed for accurate spectral-spatial classification of hyperspectral image (HSI). In this framework, one-dimensional CNN is utilized to automatically extract the hierarchical spectral features and two-dimensional CNN is applied to extract the hierarchical space-related features, and then a softmax regression classifier is used to combine the spectral and spatial features together and predict classification results eventually. To overcome the problem of the limited available training samples in HSIs, we propose a simple data augmentation method which is efficient and effective for improving HSI classification accuracy. For comparison and validation, we test the proposed method along with three other deep-learning-based HSI classification methods on two real-world HSI data sets. Experimental results demonstrate that our DC-CNN-based method outperforms the state-of-the-art methods by a considerable margin.
AB - In this article, a novel dual-channel convolutional neural network (DC-CNN) framework is proposed for accurate spectral-spatial classification of hyperspectral image (HSI). In this framework, one-dimensional CNN is utilized to automatically extract the hierarchical spectral features and two-dimensional CNN is applied to extract the hierarchical space-related features, and then a softmax regression classifier is used to combine the spectral and spatial features together and predict classification results eventually. To overcome the problem of the limited available training samples in HSIs, we propose a simple data augmentation method which is efficient and effective for improving HSI classification accuracy. For comparison and validation, we test the proposed method along with three other deep-learning-based HSI classification methods on two real-world HSI data sets. Experimental results demonstrate that our DC-CNN-based method outperforms the state-of-the-art methods by a considerable margin.
UR - http://www.scopus.com/inward/record.url?scp=85011966281&partnerID=8YFLogxK
U2 - 10.1080/2150704X.2017.1280200
DO - 10.1080/2150704X.2017.1280200
M3 - 文章
AN - SCOPUS:85011966281
SN - 2150-704X
VL - 8
SP - 438
EP - 447
JO - Remote Sensing Letters
JF - Remote Sensing Letters
IS - 5
ER -