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Received：24 September 2021，
Published：07 December 2023
移动端阅览
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。为更好地实施多源多模态遥感大数据的智能处理与解译分析，获取可服务于精准应用的时空信息，本文借鉴粒计算的数据处理思维，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、属性等3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、多粒度决策等3个基础模型的遥感粒计算方法。重点以精准农业应用需求为导向开展了实践研究，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进行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。
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 multi-level 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.
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