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Comparison Analysis on Wheat Mapping Using Deep Learning Algorithm from Different Satellite Data Source |
Gang Cui(),Jinsheng Wu(),Zhen Yu,Ling Zhou |
Survey Office of the National Bureau of Statistics in Shandong, Ji’nan, 250001, China |
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Abstract Quantitative analysis on the relationships between the remote sensing scale and the land cover classification accuracy, which is the basis for making a decision on remote sensing resolution determination, is essential for mapping the concise land cover. Up to now, deep learning is an innovative algorithm to learn the hierarchical layer features without supervised control, which is different from the traditional classifiers that require man-made labels as input. Therefore, it is interesting to explore the inherent relationship between the classification accuracy and remote sensing image spatial scale from this algorithm. In this paper, we applied a Deep Convolutional Neural Network (DCNN) which is Pyramid Scene Parsing Network (PSPNet) on four scale remote sensing image (8 m, 3.2 m, 2 m, 0.8 m) to map the wheat distribution. The experiment results showed that the PSPNet is good at learning the spatial feature without manual operations, then the wheat extent could be extracted automatically. Different from the conventional algorithm of determining the optimized spatial resolution, the PSPNet could identify the wheat better accompanying with the spatial resolution increased and more concise wheat results could be obtained. This conclusions represent that deep convolution neural network can take full use of the spatial information of the high remote sensing image to ensure the performance of wheat extent, which brings us a new idea of improving the accuracy of crop mapping adequately if we can get the super-high resolution remote sensing image.
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Received: 10 May 2018
Published: 16 October 2019
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Corresponding Authors:
Jinsheng Wu
E-mail: cuigang@stats;wjs@stats
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