Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (69)
  • Open Access

    ARTICLE

    Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

    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: More >

  • Open Access

    ARTICLE

    Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

    Sarkar Hasan Ahmed1, Adel Al-Zebari2, Rizgar R. Zebari3, Subhi R. M. Zeebaree4,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3993-4008, 2023, DOI:10.32604/cmc.2023.037464

    Abstract Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. More >

  • Open Access

    ARTICLE

    Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

    Rui Xu1,2,3, Xiaoqiang Di1,3,4,*, Hao Luo1, Hui Qi1,3, Xiongwen He5, Wenping Lei6

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3903-3922, 2023, DOI:10.32604/cmc.2023.037054

    Abstract In Information Centric Networking (ICN) where content is the object of exchange, in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks. Setting up cache space at any node enables users to access data nearby, thus relieving the processing pressure on the servers. However, the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents. To address the issues mentioned, a cooperative caching strategy… More >

  • Open Access

    ARTICLE

    Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

    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… More >

  • Open Access

    ARTICLE

    Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

    S. Rajalakshmi1,*, S. Nalini2, Ahmed Alkhayyat3, Rami Q. Malik4

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1673-1688, 2023, DOI:10.32604/csse.2023.034414

    Abstract Remote sensing image (RSI) classifier roles a vital play in earth observation technology utilizing Remote sensing (RS) data are extremely exploited from both military and civil fields. More recently, as novel DL approaches develop, techniques for RSI classifiers with DL have attained important breakthroughs, providing a new opportunity for the research and development of RSI classifiers. This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification (ISMOGCN-HRSC) model. The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs. In the presented ISMOGCN-HRSC More >

  • Open Access

    ARTICLE

    Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

    Imran Ali1, Zohaib Mushtaq2, Saad Arif3, Abeer D. Algarni4,*, Naglaa F. Soliman4, Walid El-Shafai5,6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 303-319, 2023, DOI:10.32604/csse.2023.034374

    Abstract Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on… More >

  • Open Access

    ARTICLE

    A More Efficient Approach for Remote Sensing Image Classification

    Huaxiang Song*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5741-5756, 2023, DOI:10.32604/cmc.2023.034921

    Abstract Over the past decade, the significant growth of the convolutional neural network (CNN) based on deep learning (DL) approaches has greatly improved the machine learning (ML) algorithm’s performance on the semantic scene classification (SSC) of remote sensing images (RSI). However, the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms, e.g., automation and simplicity, partially lost. Traditional ML strategies (e.g., the handcrafted features or indicators) and accuracy-aimed strategies with a high trade-off (e.g., the multi-stage CNNs and ensemble of multi-CNNs) are widely used without any training efficiency optimization involved, which… More >

  • Open Access

    ARTICLE

    Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

    Mutiullah Jamil1, Hafeez ul Rehman1, SaleemUllah1, Imran Ashraf2,*, Saqib Ubaid1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5545-5557, 2023, DOI:10.32604/cmc.2023.033449

    Abstract Wheat species play important role in the price of products and wheat production estimation. There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable. The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions. With the use of satellite remote sensing, it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle. In this work, nine popular… More >

  • Open Access

    ARTICLE

    Remote Sensing Plateau Forest Segmentation with Boundary Preserving Double Loss Function Collaborative Learning

    Ying Ma1, Jiaqi Zhang2,3,4, Pengyu Liu1,2,3,4,*, Zhihao Wei5, Lingfei Zhang1, Xiaowei Jia6

    Journal of New Media, Vol.4, No.4, pp. 165-177, 2022, DOI:10.32604/jnm.2022.026684

    Abstract Plateau forest plays an important role in the high-altitude ecosystem, and contributes to the global carbon cycle. Plateau forest monitoring request in-suit data from field investigation. With recent development of the remote sensing technic, large-scale satellite data become available for surface monitoring. Due to the various information contained in the remote sensing data, obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges. Recent developed deep learning (DL) models such as deep convolutional neural network (CNN) has been widely used in image processing tasks, and shows possibility for remote sensing segmentation. However,… More >

  • Open Access

    ARTICLE

    Remote Sensing Data Processing Process Scheduling Based on Reinforcement Learning in Cloud Environment

    Ying Du1,2, Shuo Zhang1,2, Pu Cheng3,*, Rita Yi Man Li4, Xiao-Guang Yue5,6

    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 More >

Displaying 21-30 on page 3 of 69. Per Page