粒子群优化与自适应注入模型的遥感图像融合

Remote Sensing Image Fusion Based on Particle Swarm Optimization and Adaptive Injection Model

提出了一种基于粒子群优化的融合方法,旨在解决图像融合过程中可能出现的光谱信息和空间细节信息损失、融合图像不清晰等问题。首先对原始图像进行预处理,以获取图像各通道的边缘检测矩阵;其次,利用最小二乘法计算光谱覆盖系数,以获取细节图像;最后自适应注入模型框架,引入加权矩阵,利用粒子群优化算法和综合全局相对误差(ERGAS)指数函数优化边缘检测的权重,计算数据集波段权重,生成最终的融合图像。选取了不同分辨率的遥感卫星影像(WorldView-2、GF-2和GeoEye)进行了研究,并采用5种不同融合方法进行对比实验,选用6种评价指标进行定量分析。结果表明,所提方法在主观视觉效果以及平均梯度和空间频率等客观定量评价指标上表现优于其余方法,在保留光谱和空间信息方面也取得了较好的融合效果。

To address issues,such as loss of spectral and spatial detail as well as unclear fusion results during the fusion process,a fusion method based on particle swarm optimization is proposed.The initial step of this method involves preprocessing the original image to derive edge detection matrices for each of the image’s channels.Subsequently,the spectral coverage coefficient is determined by employing the least square method to generate a more precise image.Finally,an adaptive injection model framework is proposed,which incorporates a weighted matrix,particle swarm optimization,and error relative global accuracy(ERGAS)index function to optimize the weights for edge detection.The band weights in the dataset are calculated to generate the final fused image.In this study,the performance of five fusion methods is assessed using three remote sensing satellite images of varying resolution(WorldView2,GF2,and GeoEye)by quantitatively analyzing six evaluation indicators.The results indicate that the method proposed in this paper outperforms other methods in terms of subjective visual effects and objective quantitative evaluation indicators such as average gradient and spatial frequency.Furthermore,the proposed method realizes a good fusion effect in retaining spectral and spatial information.