侯昭阳, 吕开云, 龚循强, 支君豪, 王楠. 一种结合低级视觉特征和PAPCNN的NSST域遥感影像融合方法[J]. 武汉大学学报 ( 信息科学版), 2023, 48(6): 960-969. DOI: 10.13203/j.whugis20220168
引用本文: 侯昭阳, 吕开云, 龚循强, 支君豪, 王楠. 一种结合低级视觉特征和PAPCNN的NSST域遥感影像融合方法[J]. 武汉大学学报 ( 信息科学版), 2023, 48(6): 960-969. DOI: 10.13203/j.whugis20220168
HOU Zhaoyang, LÜ Kaiyun, GONG Xunqiang, ZHI Junhao, WANG Nan. Remote Sensing Image Fusion Based on Low-Level Visual Features and PAPCNN in NSST Domain[J]. Geomatics and Information Science of Wuhan University, 2023, 48(6): 960-969. DOI: 10.13203/j.whugis20220168
Citation: HOU Zhaoyang, LÜ Kaiyun, GONG Xunqiang, ZHI Junhao, WANG Nan. Remote Sensing Image Fusion Based on Low-Level Visual Features and PAPCNN in NSST Domain[J]. Geomatics and Information Science of Wuhan University, 2023, 48(6): 960-969. DOI: 10.13203/j.whugis20220168

一种结合低级视觉特征和PAPCNN的NSST域遥感影像融合方法

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

  • 摘要: 针对融合规则中活动度量构建的单一性和脉冲耦合神经网络(pulse coupled neural network, PCNN)参数设置的主观性问题,提出一种非下采样剪切波变换(non-subsampled shearlet transform, NSST)域内结合低级视觉特征和参数自适应PCNN(parameter adaptive PCNN,PAPCNN)的遥感影像融合方法。首先将全色影像和多光谱影像YUV颜色空间的亮度分量Y通过NSST分解得到高频和低频子带,其次利用基于低级视觉特征的融合规则对低频子带进行融合,结合局部相位一致性、局部突变度量和局部能量信息3个低级特征构建新的活动度量;然后采用PAPCNN模型对高频子带进行融合,将多尺度形态梯度作为模型的外部输入信号;最后依次进行NSST逆变换和YUV逆变换,得到融合影像。实验结果表明,所提方法对不同平台和不同地面特征的遥感影像都能表现出较好的效果,相较于其他11种方法,在所有评价指标上均表现优秀。所提方法能够较好地保留原始影像中的空间信息和光谱信息,可以提供优势互补的融合影像。

     

    Abstract:
      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 multispectral 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.

     

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