计算机科学
人工智能
感知器
模式识别(心理学)
稳健性(进化)
多光谱图像
分类器(UML)
变更检测
多层感知器
集成学习
人工神经网络
生物化学
化学
基因
作者
Moumita Roy,Dipen Routaray,Susmita Ghosh,Ashish Ghosh
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2014-01-01
卷期号:11 (1): 49-53
被引量:14
标识
DOI:10.1109/lgrs.2013.2245855
摘要
In this letter, a change detection technique using a multiple classifier system is proposed. Here, different architectures of multilayer perceptron (MLP) are used as base classifiers. An ensemble of different MLPs is utilized to increase the robustness of the system. This also avoids the problem of choosing an optimum architecture for MLP. First, the support values for each of the unlabeled patterns are estimated using different MLPs (trained with the labeled patterns). Then, each of the unlabeled patterns is assigned to a specific class by fusing the outcome of the base classifiers using different combination rules. Experiments are carried out on multitemporal and multispectral images. Results show that the proposed ensemble technique has an edge over individual base classifiers for change detection in remotely sensed images.
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