TY - GEN
T1 - Joint feature extraction for multispectral and panchromatic images based on convolutional neural network
AU - Chen, Yi
AU - Zhang, Mengmeng
AU - Li, Wei
AU - Du, Qian
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - 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.
AB - 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.
KW - Convolutional Neural Network
KW - Joint Feature Extraction
KW - Multispectral Image
KW - Panchromatic Image
UR - http://www.scopus.com/inward/record.url?scp=85064178571&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8518885
DO - 10.1109/IGARSS.2018.8518885
M3 - Conference contribution
AN - SCOPUS:85064178571
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5005
EP - 5008
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -