Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 277-281, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-277-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 277-281, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-277-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

L. Ding1, H. Li2, C. Hu2, W. Zhang2, and S. Wang1 L. Ding et al.
  • 1Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, China
  • 2Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

Keywords: AlexNet, GLCM texture, Multi-kernel learning, Object-oriented classification, feature extraction ,SVM

Abstract. In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.