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  • Open Access

    ARTICLE

    Sentinel-2 Satellite Imagery Application to Monitor Soil Salinity and Calcium Carbonate Contents in Agricultural Fields

    Ahmed M. Zeyada1,*, Khalid A. Al-Gaadi1,2, ElKamil Tola2, Rangaswamy Madugundu2, Ahmed A. Alameen2

    Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1603-1620, 2023, DOI:10.32604/phyton.2023.027267

    Abstract The estuary tides affect groundwater dynamics; these areas are susceptible to waterlogging and salinity issues. A study was conducted on two fields with a total area of 60 hectares under a center pivot irrigation system that works with solar energy and belong to a commercial farm located in Northern Sudan. To monitor soil salinity and calcium carbonate in the area and stop future degradation of soil resources, easy, non-intrusive, and practical procedures are required. The objective of this study was to use remote sensing-determined Sentinel-2 satellite imagery using various soil indices to develop prediction models… More >

  • Open Access

    ARTICLE

    An Internet of Things Platform for Air Station Remote Sensing and Smart Monitoring

    David Corral-Plaza1, Juan Boubeta-Puig1, Guadalupe Ortiz1, Alfonso Garcia-de-Prado2,*

    Computer Systems Science and Engineering, Vol.35, No.1, pp. 5-12, 2020, DOI:10.32604/csse.2020.35.005

    Abstract Air pollution is currently receiving more attention by international governments and organizations. Nevertheless, current systems for air quality monitoring lack essential requirements which are key in order to be effective concerning users’ access to the information and efficient regarding real-time monitoring and notification. This paper presents an Internet of Things platform for air station remote sensing and smart monitoring that combines Big Data and Cloud Computing paradigms to process and correlate air pollutant concentrations coming from multiple remote stations, as well as to trigger automatic and personalized alerts when a health risk for their particular More >

  • Open Access

    ARTICLE

    Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

    Xiangchun Liu1, Jing Yu2,Wei Song1, 3, *, Xinping Zhang1, Lizhi Zhao1, Antai Wang4

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1385-1395, 2020, DOI:10.32604/cmc.2020.011308

    Abstract With the development of satellite technology, the satellite imagery of the earth’s surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high efficiency and low consumption. As an important tool for satellite remote sensing image processing, remote sensing image classification has become a hot topic. According to the natural texture characteristics of remote sensing images, this paper combines different texture features with the Extreme Learning Machine, and proposes a new remote sensing image classification algorithm. The experimental tests are carried out through the More >

  • Open Access

    ARTICLE

    Multi-phase Oil Tank Recognition for High Resolution Remote Sensing Images

    Changjiang Liu1, Xuling Wu2, Bing Mo1, Yi Zhang3

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 671-678, 2018, DOI:10.31209/2018.100000033

    Abstract With continuing commercialization of remote sensing satellites, the high resolution remote sensing image has been increasingly used in various fields of our life. However, processing technology of high resolution remote sensing images is still a tough problem. How to extract useful information from the massive information in high resolution remote sensing images is significant to the subsequent process. A multi-phase oil tank recognition of remote sensing images, namely coarse detection and artificial neural network (ANN) recognition, is proposed. The experimental results of algorithms presented in this paper show that the proposed processing technology is reliable More >

  • Open Access

    ARTICLE

    Hyperspectral Mineral Target Detection Based on Density Peak

    Yani Hou, Wenzhong Zhu, Erli Wang

    Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 805-814, 2019, DOI:10.31209/2019.100000084

    Abstract Hyperspectral remote sensing, with its narrow band imaging, provides the potential for fine identification of ground objects, and has unique advantages in mineral detection. However, the image is nonlinear and the pure pixel is scarce, so using standard spectrum detection will lead to an increase of the number of false alarm and missed detection. The density peak algorithm performs well in high-dimensional space and data clustering with irregular category shape. This paper used the density peak clustering to determine the cluster centers of various categories of images, and took it as the target spectrum, and More >

  • Open Access

    ARTICLE

    Robust Core Tensor Dictionary Learning with Modified Gaussian Mixture Model for Multispectral Image Restoration

    Leilei Geng1, Chaoran Cui1, Qiang Guo1, Sijie Niu2, Guoqing Zhang3, Peng Fu4, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 913-928, 2020, DOI:10.32604/cmc.2020.09975

    Abstract The multispectral remote sensing image (MS-RSI) is degraded existing multispectral camera due to various hardware limitations. In this paper, we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration. First, the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor. Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem. To improve the accuracy of core tensor coding, the core tensor estimation based on the robust modified Gaussian mixture model is introduced More >

  • Open Access

    ARTICLE

    Automatic Terrain Debris Recognition Network Based on 3D Remote Sensing Data

    Xu Han1, #, Huijun Yang1, 4, *, Qiufeng Shen1, #, 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… More >

  • Open Access

    ARTICLE

    A Weighted Threshold Secret Sharing Scheme for Remote Sensing Images Based on Chinese Remainder Theorem

    Qi He1, Shui Yu2, Huifang Xu3,*, Jia Liu4, Dongmei Huang5, Guohua Liu6, Fangqin Xu3, Yanling Du1

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 349-361, 2019, DOI:10.32604/cmc.2019.03703

    Abstract The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images. The emergence of cloud storage has brought new opportunities for storage and management of massive remote sensing images with its large storage space, cost savings. However, the openness of cloud brings challenges for image data security. In this paper, we propose a weighted image sharing scheme to ensure the security of remote sensing in cloud environment, which takes the weights of participants (i.e., cloud service providers) into consideration. An extended Mignotte sequence is constructed according to… More >

  • Open Access

    ARTICLE

    Snow Cover Mapping for Mountainous Areas by Fusion of MODIS L1B and Geographic Data Based on Stacked Denoising Auto-Encoders

    Xi Kan1, Yonghong Zhang2,*, Linglong Zhu2, Liming Xiao2, Jiangeng Wang3, Wei Tian4, Haowen Tan5

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 49-68, 2018, DOI:10.32604/cmc.2018.02376

    Abstract Snow cover plays an important role in meteorological and hydrological researches. However, the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains, due to the serious snow/cloud confusion problem caused by high altitude and complex topography. Aiming at this problem, an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau. In this work, a deep learning framework named Stacked Denoising Auto-Encoders (SDAE) was employed to fuse the MODIS multispectral images and various geographic datasets, which are then classified into three categories: Snow,… More >

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