Spatio-Temporal Data Fusion for Satellite Images Using Hopfield Neural Network

被引:13
|
作者
Fung, Che Heng [1 ]
Wong, Man Sing [1 ,2 ]
Chan, P. W. [3 ]
机构
[1] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China
[2] Hong Kong Polytech Univ, Res Inst Sustainable Urban Dev, Kowloon, Hong Kong, Peoples R China
[3] Hong Kong Observ, Hong Kong, Peoples R China
关键词
spatio-temporal data fusion; Hopfield neural network; satellite images; LAND-SURFACE TEMPERATURE; REFLECTANCE FUSION; RESOLUTION; ALGORITHM;
D O I
10.3390/rs11182077
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Spatio-Temporal Groundwater Drought Monitoring Using Multi-Satellite Data Based on an Artificial Neural Network
    Seo, Jae Young
    Lee, Sang-Il
    WATER, 2019, 11 (09)
  • [2] A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images
    Xue, Jie
    Leung, Yee
    Fung, Tung
    REMOTE SENSING, 2017, 9 (12)
  • [3] Comparison of Spatio-temporal Fusion Models of Multiple Satellite Images for Vegetation Monitoring
    Kim, Yeseul
    Park, No-Wook
    KOREAN JOURNAL OF REMOTE SENSING, 2019, 35 (06) : 1209 - 1219
  • [4] Spatio-temporal modeling and analysis of fMRI data using NARX neural network
    Luo, Huaien
    Puthusserypady, Sadasivan
    INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2006, 16 (02) : 139 - 149
  • [5] MOTION LEARNING USING SPATIO-TEMPORAL NEURAL NETWORK
    Yusoff, Nooraini
    Kabir-Ahmad, Farzana
    Jemili, Mohamad-Farif
    JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY-MALAYSIA, 2020, 19 (02): : 207 - 223
  • [6] Segmentation of Coronary Arteries Images Using Spatio-temporal Feature Fusion Network with Combo Loss
    Zhu, Hongyan
    Song, Shuni
    Xu, Lisheng
    Song, Along
    Yang, Benqiang
    CARDIOVASCULAR ENGINEERING AND TECHNOLOGY, 2022, 13 (03) : 407 - 418
  • [7] Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network
    Mojtahedi, Alireza
    Dadashzadeh, Mehran
    Azizkhani, Mostafa
    Mohammadian, Abdolmajid
    Almasi, Ramin
    ENVIRONMENTAL EARTH SCIENCES, 2022, 81 (03)
  • [8] Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network
    Alireza Mojtahedi
    Mehran Dadashzadeh
    Mostafa Azizkhani
    Abdolmajid Mohammadian
    Ramin Almasi
    Environmental Earth Sciences, 2022, 81
  • [9] Segmentation of Coronary Arteries Images Using Spatio-temporal Feature Fusion Network with Combo Loss
    Hongyan Zhu
    Shuni Song
    Lisheng Xu
    Along Song
    Benqiang Yang
    Cardiovascular Engineering and Technology, 2022, 13 : 407 - 418
  • [10] SVM spatio-temporal vegetation classification using HR satellite images
    Rejichi, S.
    Chaabane, F.
    SENSORS, SYSTEMS, AND NEXT-GENERATION SATELLITES XV, 2011, 8176