Cloud detection for Chinese high resolution remote sensing imagery using combining superpixel with convolution neural network

doi:10.13474/j.cnki.11-2246.2019.0010

Abstract

Abstract: Accurate cloud detection in Chinese high resolution remote sensing imagery is very low, due to the fact that there are few image bands and limited spectral range. In this study, high resolution remote sensing image cloud detection method based on convolution neural network is proposed. At first, in terms of network training, this paper obtains the feature of remote sensing images through using principal component transform and unsupervised pre-training network structure. Then it enters the image block into the network for cloud detection using the superpixel segmentation method for segmentation. Finally, the test image block is spliced, and the entire image cloud detection is completed. Experiments show that this method has high computational accuracy and the ranges of the spectral bands are not a limit.

Key words: high resolution remote sensing image, convolution neural network, cloud detection, superpixel segmentation

CLC Number:

P237

XU Qiheng, HUANG Yingbing, CHEN Yang. Cloud detection for Chinese high resolution remote sensing imagery using combining superpixel with convolution neural network[J]. 测绘通报, 2019, 0(1): 50-55.

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