Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 989-992, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-989-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 989-992, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-989-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

C. Yao1, Y. Zhang1, Y. Zhang1, and H. Liu2 C. Yao et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Luo Yu Road No. 129, Wuchang District, Wuhan City, Hubei Province, China
  • 2State Power Economic Research Institute, Future science and Technology City, Changping District, Beijing City, China

Keywords: Convolutional Neural Network, Deep Learning, Crop Classification, Agricultural remote sensing, High resolution image

Abstract. With the rapid development of Precision Agriculture (PA) promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN). For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.