TY - GEN
T1 - Remote Sensing Image Fusion Method Based on Adaptive Fractional Differential
AU - Li, Xiaoling
AU - Nie, Xiangfei
AU - DIng, Zegang
AU - Huang, Haibo
AU - Zhang, Yue
AU - Feng, Liyuan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - In order to resolve the issues of spectral distortion and lack of details in image fusion, a new remote sensing image fusion method based on adaptive fractional differential is proposed, which can dynamically enhance the remote sensing image according to different image features. Firstly, up-sampling operation is used in multispectral image, and the multispectral image is transformed by IHS transformation to obtain luminance component. Secondly, panchromatic image and luminance component are enhanced by adaptive fractional differential. Next, the enhanced luminance component and panchromatic image are integrated to obtained a new image, and then the new image is dealt with by histogram matching. Finally, the fused image is reconstructed by adopting the inverse IHS transformation. Experimental results indicate that the proposed method can enhance the details and textures while preserving spectral information and smooth areas in image fusion. The proposed method outperforms other traditional fusion algorithms both in visual effect and objective evaluation indexes.
AB - In order to resolve the issues of spectral distortion and lack of details in image fusion, a new remote sensing image fusion method based on adaptive fractional differential is proposed, which can dynamically enhance the remote sensing image according to different image features. Firstly, up-sampling operation is used in multispectral image, and the multispectral image is transformed by IHS transformation to obtain luminance component. Secondly, panchromatic image and luminance component are enhanced by adaptive fractional differential. Next, the enhanced luminance component and panchromatic image are integrated to obtained a new image, and then the new image is dealt with by histogram matching. Finally, the fused image is reconstructed by adopting the inverse IHS transformation. Experimental results indicate that the proposed method can enhance the details and textures while preserving spectral information and smooth areas in image fusion. The proposed method outperforms other traditional fusion algorithms both in visual effect and objective evaluation indexes.
KW - adaptive fractional differential
KW - histogram matching
KW - image processing
KW - intensity-hue-saturation transformation
KW - remote sensing image fusion
UR - http://www.scopus.com/inward/record.url?scp=85091920353&partnerID=8YFLogxK
U2 - 10.1109/ICSIDP47821.2019.9173428
DO - 10.1109/ICSIDP47821.2019.9173428
M3 - Conference contribution
AN - SCOPUS:85091920353
T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Y2 - 11 December 2019 through 13 December 2019
ER -