TY - GEN
T1 - Heterogeneous two-Stream Network with Hierarchical Feature Prefusion for Multispectral Pan-Sharpening
AU - Bai, Cloud
N1 - Funding Information:
†Corresponding author. This work was supported in part by the National Natural Science Foundation of China(61871460), the Shaanxi Provincial Key R&D Program(2020KW-003), and the Fundamental Research Funds for the Central Universities (3102019ghxm016). We thank the three anonymous reviewers for their helpful comments on an earlier draft of this paper.
Publisher Copyright:
©2021 IEEE.
PY - 2021/6/6
Y1 - 2021/6/6
N2 - Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.
AB - Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.
KW - Heterogeneous network
KW - Hierarchical feature prefusion
KW - Pan-sharpening
KW - Two-stream network
UR - http://www.scopus.com/inward/record.url?scp=85115059280&partnerID=8YFLogxK
U2 - 10.1109/icassp39728.2021.9413736
DO - 10.1109/icassp39728.2021.9413736
M3 - Conference Proceeding (Non-Journal item)
VL - 2021-June
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1845
EP - 1849
BT - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ER -