College of Information Engineering, Jinhua Polytechnic, Jinhua, China.
Pharmaceutical College, Jinhua Polytechnic, Jinhua, China.
Comput Intell Neurosci. 2023 Jan 24;2023:4874974. doi: 10.1155/2023/4874974. eCollection 2023.
Pansharpening technology is used to acquire a multispectral image with high spatial resolution from a panchromatic (PAN) image and a multispectral (MS) image. The detail injection model is popular for its flexibility. However, the accuracy of the injection gain and the extracted details may greatly influence the quality of the pansharpened image. This paper proposes an adaptive injection model to solve these problems. For detail extraction, we present a Gaussian filter estimation algorithm by exploring the intrinsic character of the MS sensor and convolving the PAN image with the filter to adaptively optimize the details to be consistent with the character of the MS image. For the adaptive injection coefficient, we iteratively adjust the coefficient by balancing the spectral and spatial fidelity. By multiplying the optimized details and injection gain, the final HRMS is obtained with the injection model. The performance of the proposed model is analyzed and a large number of tests are carried out on various satellite datasets. Compared to some advanced pansharpening methods, the results prove that our method can achieve the best fusion quality both subjectively and objectively.
融合技术用于从全色（PAN）图像和多光谱（MS）图像获取具有高空间分辨率的多光谱图像。细节注入模型因其灵活性而广受欢迎。然而，注入增益和提取的细节的准确性可能会极大地影响融合图像的质量。本文提出了一种自适应注入模型来解决这些问题。对于细节提取，我们通过探索 MS 传感器的固有特性并将 PAN 图像与滤波器进行卷积，提出了一种高斯滤波器估计算法，以自适应地优化细节，使其与 MS 图像的特征保持一致。对于自适应注入系数，我们通过平衡光谱和空间保真度来迭代调整系数。通过将优化后的细节和注入增益相乘，最终得到使用注入模型的 HRMS。分析了所提出模型的性能，并在各种卫星数据集上进行了大量测试。与一些先进的融合方法相比，结果证明我们的方法在主观和客观上都可以达到最佳的融合质量。
