Open AccessHigh Resolution Remote Sensing Image Classification Algorithm Based on Improved FCN
Author(s) -
Yaguang Kong,
Hongbo Fu,
Yangfan Yangfan,
Zhouhai,
Ning Wen,
Fan Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2006/1/012040
Subject(s) - computer science , pooling , convolution (computer science) , image (mathematics) , feature (linguistics) , artificial intelligence , pixel , algorithm , pattern recognition (psychology) , remote sensing , standardization , process (computing) , artificial neural network , geography , philosophy , linguistics , operating system
Aiming at the problems of less bands of high resolution remote sensing image data and limited learning richness of model features, this paper proposes a high resolution remote sensing image classification algorithm based on improved full convolution neural network. Firstly, a standardization layer is added to batch process the image, and then a pooling index is added to the image to realize the up-sampling. Finally, the pooling index, the transposed convolution and the convolution eigenvalue are combined into a feature group to restore the class pixels of the image to a great extent. It can improve the prediction ability of the model. A simulation experiment is carried out to verify the effectiveness and feasibility of the proposed algorithm.