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首页> 外文会议>The 28th International Symposium on Remote Sensing of Environment, Mar 27-31, 2000, Cape Town, South Africa >Integration of Spectral and Spatial Classification Methods for Model Parameterization in the ARSGISIP Project
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Integration of Spectral and Spatial Classification Methods for Model Parameterization in the ARSGISIP Project

机译：光谱和空间分类方法的集成，用于ARSGISIP项目中的模型参数化

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摘要

Distributed spatial information of land cover of a river catchment is a prerequisite input to model water, solute and erosion transport dynamics simulated by physically based models applied in the EU project "Applied Remote Sensing and GIS Integration for Model Parameterization" (ARSGISIP). Such models are applied by the project's end users, and typically require intensive and costly parameterization when dealing regionalized on a catchment scale. However, at present, due to a lack of methodical expertise, such parameterizations mainly are based on standard procedures providing these information only with low spatial and temporal resolution. This paper is focused on the derivation of land use information, in particular agricultural crop pattern and discrimination of rural built-up areas by means of multi-resolution and multi-spectral land cover classifications and texture analysis using Landsat-5 TM and IRS-1C PAN data.
机译：流域土地覆盖物的分布式空间信息是对水，溶质和侵蚀运输动力学进行建模的先决条件输入，该模型由欧盟项目“为模型参数化应用的遥感和GIS集成”（ARSGISIP）中应用的基于物理的模型进行了模拟。此类模型由项目的最终用户应用，并且在按集水区规模进行区域化处理时，通常需要密集且昂贵的参数化。但是，目前，由于缺乏方法专业知识，此类参数化主要基于仅以低时空分辨率提供这些信息的标准程序。本文着重于土地利用信息的推导，特别是农作物的种植方式和农村集约区的歧视，通过使用Landsat-5 TM和IRS-1C的多分辨率和多光谱土地覆盖分类以及纹理分析PAN数据。

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