N. A. Lawrance^*, T. S. Shiny Angel

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3439-3455, 2023, DOI:10.32604/csse.2023.030311

Abstract The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: first, the multispectral and panchromatic… More >

S. Vijayalakshmi^*, S. Magesh Kumar

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2915-2931, 2023, DOI:10.32604/iasc.2023.034165

Abstract Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation… More >

Ying Du^1,2, Shuo Zhang^1,2, Pu Cheng^3,*, Rita Yi Man Li⁴, Xiao-Guang Yue^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1965-1979, 2023, DOI:10.32604/cmes.2023.024871

Abstract Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing performance. To solve the task scheduling problem for remote sensing data processing in cloud computing, this paper proposes a workflow task scheduling algorithm---Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning (WDRL). The remote sensing data process modeling is transformed into a directed acyclic graph scheduling problem. Then, the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation method. Finally, combine the advantages of reinforcement learning and deep neural networks to minimize make-time for remote sensing data processes… More >

Xu Han^{1, #}, Huijun Yang^{1, 4, *}, Qiufeng Shen^{1, #}, Jiangtao Yang², Huihui Liang¹, Cancan Bao¹, Shuang Cang³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 579-596, 2020, DOI:10.32604/cmc.2020.011262

Abstract Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes, there still exist some challenges in the debris recognition of terrain data. Compared with hundreds of thousands of indoor point clouds, the amount of terrain point cloud is up to millions. Apart from that, terrain point cloud data obtained from remote sensing is measured in meters, but the indoor scene is measured in centimeters. In this case, the terrain debris obtained from remote sensing mapping only have dozens of points, which means that sufficient training information cannot be obtained only through the convolution of points. In… More >