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2018 Volume 43 Issue 10
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CHEN Lei-shi, ZHAO Jun-san, LI Yi, ZHU Qi-fu, XU Ke. On Land Use Classification by Means of Machine Learning Based on Multi-source Remote Sensing Image Fusion[J]. Journal of Southwest China Normal University(Natural Science Edition), 2018, 43(10): 103-111. doi: 10.13718/j.cnki.xsxb.2018.10.018
Citation: CHEN Lei-shi, ZHAO Jun-san, LI Yi, ZHU Qi-fu, XU Ke. On Land Use Classification by Means of Machine Learning Based on Multi-source Remote Sensing Image Fusion[J]. Journal of Southwest China Normal University(Natural Science Edition), 2018, 43(10): 103-111. doi: 10.13718/j.cnki.xsxb.2018.10.018
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On Land Use Classification by Means of Machine Learning Based on Multi-source Remote Sensing Image Fusion

  • Faculty of Land and Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China

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  • Received Date: 06/02/2018
  • In order to obtain accurate urban land use information quickly and improve the precision of land use classification information in high altitude areas by remote sensing image, the study deals with the exploration of the application of a rapidly developing technology, machine learning, in such fields. The main urban area of Kunming City was chosen as the case area in the research, taking Landsat8 and Sentinel-1A remote sensing image as the original data. Then the convolution neural network and BP neural network was used to extract the land use classification information of the remote sensing images before and after the fusion. After that the classification results were analyzed. Finally the results show that the classification method of convolutional neural network classification based on fused image data of Land sat 8 and Sentinel-1A had the best classification results, those overall classification accuracy and the Kappa coefficient reached 85.8091% and 0.8124. Therefore the classification method of convolutional neural network based on multi-source remote sensing image fusion is feasible to obtain accurate urban land use classification information, which provides a reference for the research of land use classification in urban areas of high altitude.
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On Land Use Classification by Means of Machine Learning Based on Multi-source Remote Sensing Image Fusion

  • Received Date: 06/02/2018

Abstract: In order to obtain accurate urban land use information quickly and improve the precision of land use classification information in high altitude areas by remote sensing image, the study deals with the exploration of the application of a rapidly developing technology, machine learning, in such fields. The main urban area of Kunming City was chosen as the case area in the research, taking Landsat8 and Sentinel-1A remote sensing image as the original data. Then the convolution neural network and BP neural network was used to extract the land use classification information of the remote sensing images before and after the fusion. After that the classification results were analyzed. Finally the results show that the classification method of convolutional neural network classification based on fused image data of Land sat 8 and Sentinel-1A had the best classification results, those overall classification accuracy and the Kappa coefficient reached 85.8091% and 0.8124. Therefore the classification method of convolutional neural network based on multi-source remote sensing image fusion is feasible to obtain accurate urban land use classification information, which provides a reference for the research of land use classification in urban areas of high altitude.

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