基于Sugeno模糊推理系统的遥感影像土地覆盖分类

IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jenicka S.
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引用次数: 0

摘要

遥感影像中土地覆盖分类的准确性取决于提取的特征和使用的分类器。纹理特征在土地覆盖分类中具有重要意义。传统的纹理模型只能捕获具有离散边界的图案,而模糊图案需要通过赋予不确定性适当的权重来进行分类。当遥感图像中含有噪声时,图像可能具有模糊的地表覆盖特征和模糊的地表覆盖边界。为此,提出了一种基于Sugeno模糊推理系统(FIS)的遥感影像土地覆盖分类模糊纹理模型。支持向量机(SVM)用于图像像素的精确和快速分类。为此,提出了一种模糊纹理模型与支持向量机相结合的遥感影像土地覆盖分类方法。在本章中,利用本文提出的纹理模型的多元版本对IRS-P6、LISS-IV遥感影像进行土地覆盖分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images
Accuracy of land cover classification in remotely sensed images relies on the features extracted and the classifier used. Texture features are significant in land cover classification. Traditional texture models capture only patterns with discrete boundaries whereas fuzzy patterns need to be classified by assigning due weightage to uncertainty. When remotely sensed image contains noise, the image may have fuzzy patterns characterizing land covers and fuzzy boundaries separating land covers. So a fuzzy texture model is proposed for effective classification of land covers in remotely sensed images and the model uses Sugeno Fuzzy Inference System (FIS). Support Vector Machine (SVM) is used for precise and fast classification of image pixels. Hence it is proposed to use a hybrid of fuzzy texture model and SVM for land cover classification of remotely sensed images. In this chapter, land cover classification of IRS-P6, LISS-IV remotely sensed image is performed using multivariate version of the proposed texture model.
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来源期刊
International Journal of Agricultural and Environmental Information Systems
International Journal of Agricultural and Environmental Information Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.70
自引率
0.00%
发文量
10
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