Super-resolution offers a new image with high resolution from the low-resolution (LR) image that is highly employed for the numerous remote sensing applications. Most of the existing techniques for formation of the super-resolution image exhibit the loss of quality and deviation from the original multi-spectral LR image. Thus, this paper aims at proposing an efficient super-resolution method using the hybrid model. The hybrid model is developed using the support vector regression model and multi-support vector neural network (MSVNN), and the weights of the MSVNN is tuned optimally using the proposed algorithm. The proposed DolLion algorithm is the integration of the dolphin echolocation algorithm and lion optimization algorithm that exhibits better convergence and offers a global optimal solution. The experimentation is performed using the datasets taken from the multi-spectral scene images. The optimal and effective formation of the super-resolution image using the proposed hybrid model outperforms the existing methods, and the analysis using the second-derivative-like measure of enhancement (SDME) ensures that the proposed method is better and yields a maximum SDME of 67.6755 dB.

中文翻译：

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S-DolLion-MSVNN：一种用于从多光谱卫星图像中生成超分辨率图像的混合模型

超分辨率提供了高分辨率的新图像，而低分辨率（LR）图像已被广泛应用于众多遥感应用中。用于形成超分辨率图像的大多数现有技术表现出质量损失和与原始多光谱LR图像的偏差。因此，本文旨在提出一种使用混合模型的有效超分辨率方法。使用支持向量回归模型和多支持向量神经网络（MSVNN）开发了混合模型，并使用所提出的算法对MSVNN的权重进行了优化。提出的DolLion算法是海豚回声定位算法和狮子优化算法的集成，具有更好的收敛性并提供了全局最优解。使用从多光谱场景图像中获取的数据集进行实验。使用所提出的混合模型优化和有效地形成超分辨率图像的性能优于现有方法，并且使用类似于二阶导数的增强措施（SDME）进行的分析确保了所提出的方法更好，并且产生了最大的SDME。 67.6755分贝。