An Encoder-Decoder with a Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images

For many urban studies it is necessary to obtain remote sensing images with high hyperspectral and spatial resolution by fusing the hyperspectral and panchromatic remote sensing images. In this article, we propose a deep learning model of an encoder-decoder with a residual network (EDRN) for remote sensing image fusion. First, we combined the hyperspectral and panchromatic remote sensing images to circumvent the independence of the hyperspectral and panchromatic image features. Second, we established an encoder-decoder network for extracting representative encoded and decoded deep features. Finally, we established residual networks between the encoder network and the decoder network to enhance the extracted deep features. We evaluated the proposed method on six groups of real-world hyperspectral and panchromatic image datasets, and the experimental results confirmed the superior performance of the proposed method versus six other methods.

Automated summary

In this study, a deep learning model of an encoder-decoder with a residual network (EDRN) is proposed for fusing hyperspectral and panchromatic remote sensing images. The experimental results demonstrate the superior performance of the proposed method on six real-world datasets.