Satellite Image Matching Method Based on Deep Convolution Neural Network

doi:10.11947/j.AGCS.2018.20170627

Abstract

Abstract: This article focuses on the first aspect of the album of deep learning: the deep convolution method.The traditional matching point extraction algorithm usually uses the manually-designed feature descriptor and the shortest distance between them to match as the matching criterion.The matching result is easy to fall into the local extreme value,which causes the missing of the partial matching point.Aiming at this problem,we introduce a two-channel deep convolution neural network based on spatial scale convolution,and performs matching pattern learning between images to realize the satellite image matching based on deep convolution neural network.The experimental results show that the method can extract the richer matching point in the case of heterogeneous,multi-temporal and multi-resolution satellite images,compared with the traditional matching method.And the accuracy of the final matching results can be maintained at above 90%.

Key words: image matching, deep learning, object-oriented, convolution neural network, satellite image

CLC Number:

P236

FAN Dazhao, DONG Yang, ZHANG Yongsheng. Satellite Image Matching Method Based on Deep Convolution Neural Network[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 844-853.

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