Artificial Intelligence-Based Deep Fusion Model for Pan-Sharpening of Remote Sensing Images. (23rd December 2021)

• Record Type: Journal Article
• Authors: Iskanderani, Ahmed I.; Mehedi, Ibrahim M.; Aljohani, Abdulah Jeza; Shorfuzzaman, Mohammad; Akhter, Farzana; Palaniswamy, Thangam; Latif, Shaikh Abdul; Latif, Abdul; Jannat, Rahtul
• Other Names: Koundal Deepika Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2021(2021)
• Publication Date: 2021-12-23
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2021/7615106
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : During the past two decades, many remote sensing image fusion techniques have been designed to improve the spatial resolution of the low-spatial-resolution multispectral bands. The main objective is fuse the low-resolution multispectral (MS) image and the high-spatial-resolution panchromatic (PAN) image to obtain a fused image having high spatial and spectral information. Recently, many artificial intelligence-based deep learning models have been designed to fuse the remote sensing images. But these models do not consider the inherent image distribution difference between MS and PAN images. Therefore, the obtained fused images may suffer from gradient and color distortion problems. To overcome these problems, in this paper, an efficient artificial intelligence-based deep transfer learning model is proposed. Inception-ResNet-v2 model is improved by using a color-aware perceptual loss (CPL). The obtained fused images are further improved by using gradient channel prior as a postprocessing step. Gradient channel prior is used to preserve the color and gradient information. Extensive experiments are carried out by considering the benchmark datasets. Performance analysis shows that the proposed model can efficiently preserve color and gradient information in the fused remote sensing images than the existing models.