Remote Sensing Image Fusion Based on Low-Level Visual Features and PAPCNN in NSST Domain

被引：0
|
作者：
Hou Z. [1 ]
Lü K. [1 ,2 ,3 ]
Gong X. [1 ,3 ]
Zhi J. [1 ]
Wang N. [1 ]
机构：
[1] Faculty of Geomatics, East China University of Technology, Nanchang
[2] Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou
[3] Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake, Ministry of Natural Resources, Nanchang
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2023年 / 48卷 / 06期
关键词：
image fusion; low-level visual; non-subsampled contourlet transform; pulse coupled neural network (PCNN); remote sensing image;
D O I：
10.13203/j.whugis20220168
中图分类号：
学科分类号：
摘要：
Objectives: In order to solve the problems of the singleness of activity metric construction in fusion rules and the subjectivity of parameter setting of pulse-coupled neural network (PCNN), a remote sensing image fusion method combining low-level visual features and parameter adaptive pulse coupled neural network (PAPCNN) in the non-subsampled shearlet transform (NSST) domain is proposed in this paper. Methods: First, the panchromatic image and the luminance component Y in YUV color space of multi-spectral image are decomposed by NSST to obtain the high and low frequency components. Second, a fusion rule based on low-level visual features is used to low-frequency components fusion, and a new activity measure is constructed by combining three low-level features, namely, local phase congruency, local abrupt measure and local energy information. Then, PAPCNN model is used to high-frequency components fusion, and the multi-scale morphological gradient is used as the external input signal of the model. Finally, the fused image is obtained through NSST inverse transform and YUV inverse transform in turn. Results: The experimental results show that this method has better performance in remote sensing images of different platforms and different ground features. Compared with other 11 methods, this method has absolute advantages in all evaluation indexes. Conclusions: The proposed method can better preserve the spatial and spectral information in the original image, thus it can provide a fused image with complementary advantages. © 2023 Wuhan University. All rights reserved.
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页码：960 / 969
页数：9
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