TY - JOUR
T1 - Spatial and Spectral Joint Super-Resolution Using Convolutional Neural Network
AU - Mei, Shaohui
AU - Jiang, Ruituo
AU - Li, Xu
AU - Du, Qian
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Many applications have benefited from the images with both high spatial and spectral resolution, such as mineralogy and surveillance. However, it is difficult to acquire such images due to the limitation of sensor technologies. Recently, super-resolution (SR) techniques have been proposed to improve the spatial or spectral resolution of images, e.g., improving the spatial resolution of hyperspectral images (HSIs) or improving spectral resolution of color images (reconstructing HSIs from RGB inputs). However, none of the researches attempted to improve both spatial and spectral resolution together. In this article, these two types of resolution are jointly improved using convolutional neural network (CNN). Specifically, two kinds of CNN-based SR are conducted, including a simultaneous spatial-spectral joint SR (SimSSJSR) that conducts SR in spectral and spatial domain simultaneously and a separated spatial-spectral joint SR (SepSSJSR) that considers spectral and spatial SR sequentially. In the proposed SimSSJSR, a full 3-D CNN is constructed to learn an end-to-end mapping between a low spatial-resolution mulitspectral image (LR-MSI) and the corresponding high spatial-resolution HSI (HR-HSI). In the proposed SepSSJSR, a spatial SR network and a spectral SR network are designed separately, and thus two different frameworks are proposed for SepSSJSR, namely SepSSJSR1 and SepSSJSR2, according to the order that spatial SR and spectral SR are applied. Furthermore, the least absolute deviation, instead of mean square error (MSE) in traditional SR networks, is chosen as the loss function for the proposed networks. Experimental results over simulated images from different sensors demonstrated that the proposed SepSSJSR1 is most effective to improve spatial and spectral resolution of MSIs sequentially by conducting spatial SR prior to spectral SR. In addition, validation on real Landsat images also indicates that the proposed SSJSR techniques can make full use of available MSIs for high-resolution-based analysis or applications.
AB - Many applications have benefited from the images with both high spatial and spectral resolution, such as mineralogy and surveillance. However, it is difficult to acquire such images due to the limitation of sensor technologies. Recently, super-resolution (SR) techniques have been proposed to improve the spatial or spectral resolution of images, e.g., improving the spatial resolution of hyperspectral images (HSIs) or improving spectral resolution of color images (reconstructing HSIs from RGB inputs). However, none of the researches attempted to improve both spatial and spectral resolution together. In this article, these two types of resolution are jointly improved using convolutional neural network (CNN). Specifically, two kinds of CNN-based SR are conducted, including a simultaneous spatial-spectral joint SR (SimSSJSR) that conducts SR in spectral and spatial domain simultaneously and a separated spatial-spectral joint SR (SepSSJSR) that considers spectral and spatial SR sequentially. In the proposed SimSSJSR, a full 3-D CNN is constructed to learn an end-to-end mapping between a low spatial-resolution mulitspectral image (LR-MSI) and the corresponding high spatial-resolution HSI (HR-HSI). In the proposed SepSSJSR, a spatial SR network and a spectral SR network are designed separately, and thus two different frameworks are proposed for SepSSJSR, namely SepSSJSR1 and SepSSJSR2, according to the order that spatial SR and spectral SR are applied. Furthermore, the least absolute deviation, instead of mean square error (MSE) in traditional SR networks, is chosen as the loss function for the proposed networks. Experimental results over simulated images from different sensors demonstrated that the proposed SepSSJSR1 is most effective to improve spatial and spectral resolution of MSIs sequentially by conducting spatial SR prior to spectral SR. In addition, validation on real Landsat images also indicates that the proposed SSJSR techniques can make full use of available MSIs for high-resolution-based analysis or applications.
KW - Convolutional neural network (CNN)
KW - hyperspectral image (HSI)
KW - multispectral image (MSI)
KW - spatial-spectral
KW - super-resolution (SR)
UR - http://www.scopus.com/inward/record.url?scp=85086888787&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2020.2964288
DO - 10.1109/TGRS.2020.2964288
M3 - 文章
AN - SCOPUS:85086888787
SN - 0196-2892
VL - 58
SP - 4590
EP - 4603
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 7
M1 - 8974222
ER -