Artificial Intelligence-Based Deep Fusion Model for Pan-Sharpening of Remote Sensing Images

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) i...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2021, Vol.2021 (1), p.7615106-7615106
Main Authors: Iskanderani, Ahmed I., Mehedi, Ibrahim M., Aljohani, Abdulah Jeza, Shorfuzzaman, Mohammad, Akhter, Farzana, Palaniswamy, Thangam, Latif, Shaikh Abdul, Latif, Abdul, Jannat, Rahtul
Format: Article
Language:English
Subjects:
Analysis
Artificial Intelligence
Color
Computer vision
Deep learning
Dictionaries
Discriminant analysis
Image processing
Image resolution
Literature reviews
Neural networks
Optimization
Remote sensing
Remote Sensing Technology
Sharpening
Spatial discrimination
Spatial resolution
Transfer learning
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2021/7615106