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Search Results (18)
  • Open Access


    A Consistent Mistake in Remote Sensing Images’ Classification Literature

    Huaxiang Song*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1381-1398, 2023, DOI:10.32604/iasc.2023.039315

    Abstract Recently, the convolutional neural network (CNN) has been dominant in studies on interpreting remote sensing images (RSI). However, it appears that training optimization strategies have received less attention in relevant research. To evaluate this problem, the author proposes a novel algorithm named the Fast Training CNN (FST-CNN). To verify the algorithm’s effectiveness, twenty methods, including six classic models and thirty architectures from previous studies, are included in a performance comparison. The overall accuracy (OA) trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline. Results show that there is a maximal OA… More >

  • Open Access


    Optimizing Spatial Relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images

    Zimeng Yang, Qiulan Wu, Feng Zhang*, Xuefei Chen, Weiqiang Wang, Xueshen Zhang

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 491-506, 2023, DOI:10.32604/iasc.2023.037558

    Abstract Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote sensing images (called DGCN hereinafter),… More >

  • Open Access


    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. At the initial stage, the… More >

  • Open Access


    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 approach for classifying the vegetation… More >

  • Open Access


    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 model, the synergic deep learning… More >

  • Open Access


    Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

    Tianyu Liu1,3,4, Pengyu Liu1,2,3,4,*, Xiaowei Jia5, Shanji Chen2, Ying Ma2, Qian Gao1,3,4

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1033-1045, 2023, DOI:10.32604/csse.2023.028225

    Abstract Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction, the monitoring of near-shore marine environment, and near-shore target recognition. To mitigate large number of parameters and improve the segmentation accuracy, we propose a new Squeeze-Depth-Wise UNet (SDW-UNet) deep learning model for sea-land remote sensing image segmentation. The proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules, which enhance the model capacity in combining multiple channels and reduces the model parameters. We further explore the effect of position-encoded information in NLP (Natural Language Processing) domain on sea-land… More >

  • Open Access


    Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

    Mesfer Al Duhayyim1,*, Hadeel Alsolai2, Siwar Ben Haj Hassine3, Jaber S. Alzahrani4, Ahmed S. Salama5, Abdelwahed Motwakel6, Ishfaq Yaseen6, Abu Sarwar Zamani6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3167-3181, 2023, DOI:10.32604/cmc.2023.033054

    Abstract Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they… More >

  • Open Access


    Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

    Saeed Masoud Alshahrani1, Saud S. Alotaibi2, Shaha Al-Otaibi3, Mohamed Mousa4, Anwer Mustafa Hilal5,*, Amgad Atta Abdelmageed5, Abdelwahed Motwakel5, Mohamed I. Eldesouki6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3117-3131, 2023, DOI:10.32604/cmc.2023.033038

    Abstract Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately… More >

  • Open Access


    A Multi Moving Target Recognition Algorithm Based on Remote Sensing Video

    Huanhuan Zheng1,*, Yuxiu Bai1, Yurun Tian2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 585-597, 2023, DOI:10.32604/cmes.2022.020995

    Abstract The Earth observation remote sensing images can display ground activities and status intuitively, which plays an important role in civil and military fields. However, the information obtained from the research only from the perspective of images is limited, so in this paper we conduct research from the perspective of video. At present, the main problems faced when using a computer to identify remote sensing images are: They are difficult to build a fixed regular model of the target due to their weak moving regularity. Additionally, the number of pixels occupied by the target is not enough for accurate detection. However,… More >

  • Open Access


    Segmentation of Remote Sensing Images Based on U-Net Multi-Task Learning

    Ni Ruiwen1, Mu Ye1,2,3,4,*, Li Ji1, Zhang Tong1, Luo Tianye1, Feng Ruilong1, Gong He1,2,3,4, Hu Tianli1,2,3,4, Sun Yu1,2,3,4, Guo Ying1,2,3,4, Li Shijun5,6, Thobela Louis Tyasi7

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3263-3274, 2022, DOI:10.32604/cmc.2022.026881

    Abstract In order to accurately segment architectural features in high-resolution remote sensing images, a semantic segmentation method based on U-net network multi-task learning is proposed. First, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building. The remote sensing image and its truth map were used as the input in the U-net network, followed by the addition of the building ground prediction layer at the end of the U-net network. Based on the ResNet network, a multi-task network with the boundary distance prediction layer was built. Experiments involving the ISPRS aerial… More >

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