Volume 13 Number 1 (Jan. 2018)
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JCP 2018 Vol.13(1): 44-48 ISSN: 1796-203X
doi: 10.17706/jcp.13.1.44-48

Deep Learning-Based Classification of Remote Sensing Image

Jian-min Liu1, 2, Min-hua Yang1
1School of Geosciences and Info-Physics, Central South University, Changsha, 41000, China
2School of Information, Hunan Institute of Humanities Science and Technology, Loudi, 417000, China


Abstract—Deep Learning networks have sharply increased over the past 10 years, and deep Learning-Based Classification of Remote Sensing Image has attracted extensive interest. We trained a multilayer deep learning network to classify the 8 thousand unlabeled remote sensing images from Internet into the 600 different classes. In order to improve the efficiency, and shorten the experiment time, we also used pre-trained model, NVIDIA GTX970 GPU, 32GB internal memory, 10T hard-disk. We achieved error rates of 9.7% which work went relatively well than the traditional machine learning techniques. Deep learning-based network can achieve the classification of unlabeled data without any manual intervention. Compared to those usual machine learning algorithm, accuracy and speed of deep learning-based classification network is more faster and accurately.

Index Terms—Deep Learning network (DLN), remote sensing image, classification.

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Cite: Jian-min Liu, Min-hua Yang, "Deep Learning-Based Classification of Remote Sensing Image," Journal of Computers vol. 13, no. 1, pp. 44-48 , 2018.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Cherry L. Chen
Abstracting/ Indexing: DBLP, EBSCO, DOAJ, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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