Dimensionality Reduction via Regression in Hyperspectral Imagery
نویسندگان
چکیده
منابع مشابه
An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation
With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact,...
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Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In thi...
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There are a number of reasons why reduction of large data sets is necessary, for example the amount of data may be too large for some data mining programs. Something the amount of data may exceed the processing capability of a program, as it is usual in the case of hyperspectral images. The data has generally a large number of variables to analyze, some of which have more input than others. It ...
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Conventionally, pattern recognition problems involve both samples and features that get collected over time or that gets generated from distributed sources. The system starts to falter when the number of features reaches a certain threshold and exhibits the curse of dimensionality. Traditionally dimensionality reduction (DR) is performed to prevent the curse of dimensionality when all features ...
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ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Signal Processing
سال: 2015
ISSN: 1932-4553,1941-0484
DOI: 10.1109/jstsp.2015.2417833