Paper
29 December 2008 Application of immune network theory for target-oriented multi-spectral remote sensing information mining
Qing-jie Liu, Qi-zhong Lin
Author Affiliations +
Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72853V (2008) https://doi.org/10.1117/12.812392
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China
Abstract
To use target information for space transformation in remote sensing data field, artificial immune network theory is introduced to multi-spectral remote sensing information mining, based on the knowledge of target spectrum. First, the target spectrums are fuzzy clustered into several subclasses, to retain different features of target in different subclasses. Then we develop a novel Regional-memory-pattern Artificial Immune Idiotypic Network (RAIN) model based on artificial idiotypic network theory, and train RAIN with subclasses samples. And then, the affinities of the target spectrum and other objects can be calculated according to the immune microscopic dynamics including stimulation and suppression effect. Finally, principal component analysis (PCA) is performed to affinities to explore more weak and hidden information. With its application in Baoguto Area, Xinjiang Uyghur Autonomous Region China, choosing tuffaceous siltstone as target object, the result supports the efficiency of the RAIN-affinity-PCA scheme.
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Qing-jie Liu and Qi-zhong Lin "Application of immune network theory for target-oriented multi-spectral remote sensing information mining", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72853V (29 December 2008); https://doi.org/10.1117/12.812392
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KEYWORDS
Remote sensing

Principal component analysis

Data hiding

Mining

Fuzzy logic

RGB color model

Silver

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