High Dimensional Feature for Hyperspectral Image Classification
نویسندگان
چکیده
منابع مشابه
Semi-supervised feature learning for hyperspectral image classification
Hyperspectral image has high-dimensional Spectral–spatial features, those features with some noisy and redundant information. Since redundant features can have significant adverse effect on learning performance. So efficient and robust feature selection methods are make the best of labeled and unlabeled points to extract meaningful features and eliminate noisy ones. On the other hand, obtaining...
متن کاملUsing a Laboratorial Hyperspectral Image for the Evaluation of Feature Reduction Methods for the Classification of High Dimensional Data
Thee rapid advances in hyperspectral sensing technology have made it possible to collect remote sensing data in hundreds of bands. However, the data analysis methods which have been successfully applied to multispectral data are often limited to achieve satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hug...
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We study the multi-view feature extraction (MV-FE) framework for the classification of hyperspectral images acquired from airborne and spaceborne sensors. This type of data is naturally composed by distinct blocks of spectral channels, forming the hypercube. To reduce the dimensionality of the data by taking advantage of this particular structure, an unsupervised multi-view feature extraction m...
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Feature extraction plays an essential role in high-dimensional data classification. Linear discriminant analysis (LDA) is one of the most well-known methods for reducing data dimensionality in various fields. However, there are three inherent limitations when applying LDA to extract features. First, the number of features that can be extracted by LDA is the number of classes minus one at most. ...
متن کاملHyperspectral Image Classification
Article history: Received 12 October 2014 Received in revised form 26 December 2014 Accepted 1 January 2015 Available online 25 February 2015
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ژورنال
عنوان ژورنال: MATEC Web of Conferences
سال: 2018
ISSN: 2261-236X
DOI: 10.1051/matecconf/201824603041