Erratum: Hyperspectral Data Feature Extraction Using Rational Function Curve Fitting

Signal detection Using Rational Function Curve Fitting

In this manuscript, we proposed a new scheme in communication signal detection which is respect to the curve shape of received signal and based on the extraction of curve fitting (CF) features. This feature extraction technique is proposed for signal data classification in receiver. The proposed scheme is based on curve fitting and approximation of rational fraction coefficients. For each symbo...

Application of Curve Fitting in Hyperspectral Data Classification and Compression

Regarding to the high between-band correlation and large volumes of hyperspectral data, feature reduction (either feature selection or extraction) is an important part of classification process for this data type. A variety of feature reduction methods have been developed using spectral and spatial domains. In this paper, a feature extracting technique is proposed based on rational function cur...

Classification of Hyperspectral Data using Best-bases Feature Extraction Algorithms

Hyperspectral Data Classification Using Nonparametric Weighted Feature Extraction

In this paper, a new nonparametric feature extraction method is proposed for high dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features de...

Polyline Feature Extraction for Land Cover Classification using Hyperspectral Data

Prediction of landcover types from airborne/spaceborne sensors is an important classification problem in remote sensing. Due to recent advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in ∼200 bands, each of which measures the integrated response of a target over a narrow window of the electromagnetic spectrum. This unprecedented spectral resolution ...