A comparison of fuzzy and neuro-fuzzy data fusion for flooded area mapping using SAR images

被引：11

作者：

Amici, G ^{[1
]}

Dell'Acqua, F ^{[1
]}

Gamba, P ^{[1
]}

Pulina, G ^{[1
]}

机构：

[1] Univ Pavia, Dept Elect, I-27100 Pavia, Italy

来源：

INTERNATIONAL JOURNAL OF REMOTE SENSING | 2004年 / 25卷 / 20期

关键词：

Data-level fusion - Neuro-fuzzy data;

D O I：

10.1080/01431160412331269634

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

A comparison of change detection approaches for flooded area mapping using Synthetic Aperture Radar (SAR) images is provided. The aim was to assess the usefulness of fuzzy and neuro-fuzzy techniques for classification of SAR data. The work addresses both options of data-level fusion and decision-level fusion. The former is realized with multitemporal fuzzy or neural classification and the latter by combining classifications or fuzzy memberships for the pre- and post-event images. Highest overall accuracy values and flooded area accuracy values (90.3% producer's, 71.9% user's) were obtained from the neuro-fuzzy approach.

引用

页码：4425 / 4430

页数：6

共 15 条

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