遥感图谱认知理论与计算

近年来,随着对地观测技术的迅猛发展,卫星遥感领域逐渐进入了大数据时代。针对当前遥感应用的需求和特点,开展与视觉认知相结合的高分辨率遥感认知理论与方法研究是可行且必要的。在此背景下,受地学信息图谱思想启发,本文对遥感认知领域的图谱问题进行了研究,系统地提出了遥感图谱认知理论与计算方法论,旨在规范高分辨率遥感信息提取流程,构建精细化、定量化、智能化、综合化相结合的遥感信息解译模型。整套方法体系由横向“自底向上的分层抽象”和纵向“自顶向下的知识迁移”2个方向上的认知计算组成,分别对应了“由谱聚图”、“图谱协同”和“认图知谱”3大图谱转化过程。论文对涉及的概念、基本思想、关键技术及难点问题进行了重点分析,强调综合利用大数据、逐步融入知识来实现不同层次的遥感认知,以期为数据源极大丰富条件下的遥感信息解译提供新的视角。

The Theory and Calculation of Spatial-spectral Cognition of Remote Sensing

In recent years, with the rapid development of earth observation technologies, remote sensing using the satellites has gradually entered the era of big data. Facing the current demands and characteristics of remote sensing applications, it is feasible and necessary to explore the theories and methods of high-spatial-resolution remote sensing cognition with the cooperation of visual cognition. In this context, we are inspired by Geo-informatic-Tupu and intend to study the spatial-spectral cognition of remote sensing. This paper systematically presents the theory and calculation methodology for the spatial-spectral cognition of remote sensing, and expects to standardize the processes of remote sensing information extraction, as a result to further build a sophisticated, quantitative, intelligent and integrated model for the remote sensing information interpretation. The whole methodology contains two directions' cognitive calculation, namely horizontal "bottom-up hierarchical abstraction" and longitudinal "top-down knowledge transfer". These two steps are corresponded with three principal Spatial-Spectral transformation processes, which are summarized as "extracting spatial maps based on clustering pixels' spectrum", "coordinating spatial-spectral features" and "understanding attributes through the recognition of known diagram". Our study focuses on the analysis of the involved concepts, the basic idea, the key technologies and their existing difficulties, and emphasizes on the utilization of big data and gradually the application of integrated knowledge to achieve different levels of remote sensing cognition. Through these approaches, we expect to provide a new perspective for the remote sensing interpretation with the adoption of big data resources.