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引用本文:

DOI:

10.11834/jrs.2021622

收稿日期:

2021-09-23

修改日期:

2021-11-10

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基于地理图斑的遥感粒计算与精准应用
吴田军1, 骆剑承2, 张新2, 董文2, 黄启厅3, 周亚男4, 刘巍2, 孙营伟5, 杨颖频6, 胡晓东2, 郜丽静2
1.长安大学 理学院;2.中国科学院空天信息创新研究院 遥感科学国家重点实验室;3.广西农业科学院 农业科技信息研究所;4.河海大学 水文水资源学院;5.中国农业科学院 农业资源与农业区划研究所;6.广州大学 地理科学与遥感学院
摘要:

当今遥感技术实现了对地球表面进行全覆盖影像记录(空间)、快速信息更新(时间)、多手段协同观测(属性)的大数据获取,随之由遥感数据向地理信息与知识转换的鸿沟问题日益凸显。以数据粒化为基础的粒计算是大数据处理领域模拟人类思考和解决大规模复杂问题的前沿方向,其通过结构化、关联化等手段提升模式挖掘与知识发现的精度与效率。本文遵照从“外在场景的视觉理解”到“内在机理的知识发现”的演进脉络,在空间、时间、属性三个维度上剖析了遥感大数据的粒结构及其多层次、多粒度特征,并以“地理图斑”为主线发展了集成“分区分层感知、时空协同反演、多粒度决策”三个基础模型的遥感粒计算方法。面向精准农业应用的案例从多个视角阐释了粒计算契合遥感大数据智能计算的需要,验证了本文构建的理论与方法可对农业遥感多层次的复杂问题实现有序解构与逐步求解,彰显了其助益于领域化精准应用的潜在能力。

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

Currently, remote sensing technology has realized the acquisition of big data for full coverage image recording (space), rapid information updating (time) and multiple measure collaborative observation (attribute) on the earth surface. Meanwhile, the gap between remote sensing data and geographic information and knowledge is becoming increasingly prominent. 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 to improve the accuracy and efficiency of pattern mining and knowledge discovery by means of structure and association. According to the evolution route from "visual understanding of external scene" to "relationship perspective of internal generation mechanism (spectrum analysis)", this paper analyzes the granular structure of remote sensing big data and its multi-level and multi-granularity characteristics from three dimensions of space, time and attribute. In addition, we built 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". The case for precision agriculture application shows that granular computing meets the needs of remote sensing big data intelligent computing from multiple perspectives. It is verified that the theory and method proposed in this paper can realize orderly deconstruction and step-by-step solution for the multi-level complex problems of agricultural remote sensing. The case study also demonstrates its potential ability to help domain precision application.

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