基于多输入密集连接神经网络的遥感图像时空融合算法

doi:10.16451/j.cnki.issn1003-6059.201905005

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Abstract To solve the spatial-temporal fusion problem of images of surface reflectivity remote sensing satellites Landsat and MODIS, a spatial-temporal fusion algorithm for remote sensing images based on multi-input dense connected neural network is proposed. Firstly, a multi-input dense connected neural network is put forward to study the remote sensing images containing the difference information between continuous moments. Then, two transition images learned from the network are fused with the two known high spatial resolution images based on the difference similarity hypothesis to obtain the final predicted images. According to the fusion experiment of Landsat remote sensing images and MODIS remote sensing images, the proposed algorithm produces promising results in each quantitative index. The final predicted image by the proposed algorithm is more robust to noise with better recovered detail information.

Key words： Remote Sensing Image Deep Learning Spatial-Temporal Fusion Dense Neural Network

Received: 05 March 2019

ZTFLH:	TN 911.71
	TP 183

Fund:Supported by National Natural Science Foundation of China(No.61672477)

Corresponding Authors: (CAO Feilong(Corresponding author), Ph.D., professor. His research interests include deep learning and image processing.)

About author:: (YAO Kaixuan, master student. His research interests include deep learning and image processing.)

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	YAO Kaixuan¹
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Cite this article:

YAO Kaixuan¹,CAO Feilong¹. Spatial-Temporal Fusion Algorithm for Remote Sensing Images Based on Multi-input Dense Connected Neural Network[J]. , 2019, 32(5): 429-435.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201905005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I5/429

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