遥感影像智能图解及其地学认知问题探索

本文提出遥感地学智能图解模型 (RSIGIM)概念 ,主要内容是如何模拟地学专家对遥感影像的综合解译和分析过程 ,对遥感影像中包含的地物目标进行描述、识别、分类和解释 ,提取遥感影像中地物目标所属的类别 ,判别其大小、结构、相互关系等地学属性及遥感成像机理、内部特征 ,进一步融合地学分析模型 ,预测地理现象和地理过程的发展趋势 ,作出决策性规划。 RSIGIM的核心问题——遥感地学智能图解中的认知模型 ,具有层次结构 ,从低到高包括基于数理统计的影像基本处理和分析模型、基于神经计算模型影像视觉生理认知模型和基于符号知识逻辑心理认知模型等三个层次的分析认知模型。

Remote-Sensing Intelligent Geo-Interpretation Model and its Geo-Cognition Issue

In this study, the notion of Remote Sensing Intelligent Geo Interpretation Model (RSIGIM) is firstly presented. The main task of RSIGIM is to automatically simulate the human's interpretation to the remote sensing image. The initial target of RSIGIM is to describe, recognize, extract, classify and interpret the feature from RS image, and further to identify Geo properties and inner characters of the feature, such as size, structure, relationship. Integrated with geographical analysis model, the end target aims to mine out the geographical phenomenon, to predict developing process hidden in the large capacity of spatial data. The crucial issue of RSIGIM is to establish its cognition model, which is initially designed as a hierarchical structure. From low layer to high layer, three components, including statistical processing and analyzing model, neural computation and evolutionary computation based vision physiological cognition model, symbolic knowledge logic reasoning based vision psychological cognition model, are integrated to constitute relatively perfect cognition model.