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Improvement of Land Cover Classification Accuracy by using High Resolution Multi Sensor Fused Image.

使用高分辨率多传感器融合图像提高土地覆盖分类精度。

基本信息

批准号：
09680513
负责人：
HOSOMURA Tsukasa
金额：
$ 1.79万
依托单位：
Kanazawa Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1998
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09680513/
关键词：
High resolution satellite image Image fusion Land cover classification Classification category Classification accuracy Test site Classification algorithm Texture analysis テクス4p解析

项目摘要

Preparation high resolution satellite image of the simulation image using the aerial photograph is the situation which can not be obtained by postponements and failure of the satellite launch, etc. in the March 1999. In this study, the simulation image was made from aerial photograph, and various examination was carried out on the basis of this.The clustering was applied for examination fusion image on the classification item, and the examination on the classification item number was carried out. Since it changes by degree of separation of each class, as this result, it became clear that it greatly depended on what kind of land cover is included for the object image, for the classification item number.Test site data of ground resolution 1m was made in order to verify renewal classification accuracy of the test site data.By doing the schooling in the training data in the maximum likelihood method used for examination land cover classification of sorting algorithm the best, the improvement in the classification accuracy was attempted by correcting so that the training data may approximate to the normal distribution, and carrying out the classification.As a result of verification classification of the classification result, the drastic improvement in the classification accuracy could not be recognized. Then, it became clear to be one of the reasons why the classification accuracy lowers mixing pixel classification accuracy.

利用航空照片制作模拟图像的高分辨率卫星图像是由于1999年3月卫星发射推迟和失败等原因而无法获得的情况。本研究利用航空照片制作仿真图像，并在此基础上进行各种检验，对融合图像的分类项目进行聚类检验，对分类项目数进行检验。由于它根据每个类别的分离程度而变化，因此，很明显，它在很大程度上取决于对象图像中包含的土地覆盖类型，为了验证试验场数据更新分类的精度，制作了地面分辨率为1m的试验场数据，通过对训练数据进行训练，采用最大似然法对试验场进行分类分类算法的覆盖分类最好，通过校正以使训练数据可以近似于正态分布并执行分类来尝试分类精度的改进。作为分类结果的验证分类的结果，不能识别分类精度的急剧改进。然后，变得清楚的是分类精度降低混合像素分类精度的原因之一。