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) 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.

Investigation Of Hydraulic Flow Characteristics On Drop Structures

Drop structures are required if the slope of the ground level is steeper than the maximum allowable gradient channel. Drop structures become bigger as height increases. Its hydraulic capability may be reduced due to variations of jets falling on the stilling basin floor due to discharge changing. Drop structures should not be used if the change in energy level exceeds 1.50 m. The free-falling overflow on drop structures will hit the stilling basin and move downstream. As a result of overflows and turbulence in the pool below the nappe, some energy is dissipated at the front. The rest of the energy will be reduced downstream. The objectives of this study are to investigate the hydraulics flow behavior in straight and sloping drop structures and to investigate hydraulics flow behavior in a single and serial vertical drop (stepped drop). The hydraulic model results of single and stepped drop structures are compared to obtain flow behavior and energy dissipation information. The comparisons are specific to the flow parameters, including flow depth at the drop structures toe, flow depth after the jump, and hydraulic jump length.