Remote sensing granular computing and precise applications based on geo-parcels

被引：0

作者：

Wu T. ^{[1
]}

Luo J. ^{[2
,3
]}

Zhang X. ^{[2
,3
]}

Dong W. ^{[2
]}

Huang Q. ^{[4
]}

Zhou Y. ^{[5
]}

Liu W. ^{[2
]}

Sun Y. ^{[6
]}

Yang Y. ^{[7
]}

Hu X. ^{[2
]}

Gao L. ^{[2
]}

机构：

[1] School of Land Engineering, Chang’an University, Xi’an

[2] State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

[3] University of Chinese Academy of Sciences, Beijing

[4] Agricultural Science and Technology Information Research Institute, Guangxi Academy of Agricultural Sciences, Nanning

[5] School of Geography and Remote Sensing, Hohai University, Nanjing

[6] China Institute of Water Resources and Hydropower Research, Beijing

[7] School of Geography and Remote Sensing, Guangzhou University, Guangzhou

来源：

National Remote Sensing Bulletin | 2023年 / 27卷 / 12期

基金：

中国国家自然科学基金;

关键词：

geo-parcel; granular structure/granular computing; multi-granularity decision making; precision agriculture application; remote sensing big data; spatiotemporal collaborative inversion; zonal-stratified perception;

D O I：

10.11834/jrs.20211622

中图分类号：

学科分类号：

摘要：

Granular computing with data granulation as the basic is a frontier direction in the field of big data processing, which simulates human thinking and solves large-scale complex problems. It helps improve the accuracy and efficiency of pattern mining and knowledge discovery by means of structure and association. Therefore, incorporating this data analysis method into the process of mining information and discovering knowledge from remote sensing big data needs to be considered. In order to better implement intelligent processing and interpretation analysis of multi-source and multimodal remote sensing big data, and obtain spatiotemporal information that can serve precise applications, this study draws on the data processing thinking of granular computing, and builts a research path that follows the evolution route from visual understanding of external scene to relationship perspective of internal generation mechanism (spectrum analysis). The paper analyzes the granular structure of remote sensing big data and its multilevel and multi-granularity characteristics from three dimensions of space, time, and attribute. We further determine the corresponding granulation strategy based on the characteristics of remote sensing data. In addition, we build a methodology of remote sensing granular computing based on geo-parcels, which integrates the basic models of zonal-stratified perception, spatiotemporal collaborative inversion, and multi-granularity decision making. These models integrate geographical analysis methods, remote sensing mechanism models, and artificial intelligence algorithms. They also mine geographic information or knowledge including morphology, type, index, state, development trend, and mechanism of land geo-parcels. This study focuses on practical research guided by the application needs of precision agriculture. The case study shows that granular computing meets the requirements of intelligent computing of remote sensing big data from multiple perspectives. It is verified that the theory and method proposed in this study can systematically deconstruct and methodically address the multi-level complex problems of agricultural remote sensing. The case study also demonstrates its potential ability to support precise domain applications. This study develops a methodology of remote sensing intelligent computing under the guidance of granular computing. The corresponding problems and solutions in the aspects of space, time, and attribute are also analyzed. Based on the abovementioned work, we are confident that the proposed methodology of intelligent interpretation of remote sensing based on granular computing can effectively address and resolve complex surface cognitive problems in Earth observation through remote sensing. © 2023 Science Press. All rights reserved.

引用

页码：2774 / 2795

页数：21

共 38 条

[1] Cai B F, Yu R., Advance and evaluation in the long time series vegetation trends research based on remote sensing, Journal of Remote Sensing, 13, 6, pp. 1170-1186, (2009)
[2] Dong W, Wu T J, Luo J C, Sun Y W, Xia L G., Land parcel-based digital soil mapping of soil nutrient properties in an alluvialdiluvia plain agricultural area in China, Geoderma, 340, pp. 234-248, (2019)
[3] Fu B J., Geography: from knowledge, science to decision making support, Acta Geographica Sinica, 72, 11, pp. 1923-1932, (2017)
[4] Geng L Y, Ma M G., Advance in method comparison of reconstructing remote sensing time series data sets, Remote Sensing Technology and Application, 29, 2, pp. 362-368, (2014)
[5] He Z, Xie G X, Lin Y J, Huang Q T, Yang S E., Summary of the application of remote sensing in Chinese sugarcane industry, Land and Resources Informatization, 4, pp. 22-27, (2020)
[6] Hua Y X., The core problems and key technologies of pan-spatial information system, Journal of Geomatics Science and Technology, 33, 4, pp. 331-335, (2016)
[7] Hua Y X, Zhou C H., Description frame of data model of multi-granularity spatio-temporal object for pan-spatial information system, Journal of Geo-Information Science, 19, 9, pp. 1142-1149, (2017)
[8] Jiang N, Fang C, Chen M J., Initial exploration of pan-spatial cognition and representation, Journal of Geo-Information Science, 19, 9, pp. 1150-1157, (2017)
[9] Li D R., On space-air-ground integrated earth observation network, Journal of Geo-Information Science, 14, 4, pp. 419-425, (2012)
[10] Li D R, Wang M, Shen X, Dong Z P., From earth observation satellite to earth observation brain, Geomatics and Information Science of Wuhan University, 42, 2, pp. 143-149, (2017)

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