Joint Feature Extraction for Multispectral and Panchromatic Images Based on Convolutional Neural Network

摘要

Along with very high-resolution satellites were launched frequently, such as the satellite WorldView-3, panchromatic and multispectral remote-sensing images can be acquired easily. However, it is still an interesting and challenging task to fuse and classify these images. In general, panchromatic image has a high spatial resolution, but with only one spectral band. Multispectral image usually has four or eight bands, but the spatial resolution is four times smaller than panchromatic image. In this paper, an unsupervised feature extraction framework is proposed, which combines multispectral (MS) image and panchromatic (PAN) image into convolution neural network (CNN). There is an image-to-image mapping, learning from the input source (i.e., MS) to the output source (i.e., PAN). Then, by integrating the hidden layer of deep CNN, the extracted features represent MS and PAN data. The experimental results of two practical remote sensing data sets show the validity of the framework.