Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction

Yin, Jingwei; Gao, Chao; Jia, Xiuping

doi:10.1109/lgrs.2011.2179005

Cited by 35 publications

(3 citation statements)

References 30 publications

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“…Then, according to Equation (11), the same random projection matrix R iter is used to reduce the dimensionality of the hyperspectral remote sensing image A to obtain the low-dimensional image B iter . So far, the feature mean vector and low-dimensional images of all classes of low-dimensional samples can be obtained.…”

Section: Entropy-weighted Ensemble Algorithmmentioning

confidence: 99%

“…Traditional dimensionality reduction methods can be roughly divided into two types: band selection [8][9][10] and feature extraction based on data transformation [11][12][13]. The first type of method is generally based on a certain evaluation criterion function to perform a band combination search to achieve the purpose of dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

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TRP-Oriented Hyperspectral Remote Sensing Image Classification Using Entropy-Weighted Ensemble Algorithm

Jia

Zhao

et al. 2023

Remote Sensing

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The problem that the randomly generated random projection matrix will lead to unstable classification results is addressed in this paper. To this end, a Tighter Random Projection-oriented entropy-weighted ensemble algorithm is proposed for classifying hyperspectral remote sensing images. In particular, this paper presents a random projection matrix selection strategy based on the separable information of a single class able to project the features of a certain class of objects. The projection result is measured by the degree of separability, thereby obtaining the low-dimensional image with optimal separability of the class. After projecting samples with the same random projection matrix, to calculate the distance matrix, the Minimum Distance classifier is devised, repeating for all classes. Finally, the weight of the distance matrix is considered in ensemble classification by using the information entropy. The proposed algorithm is tested on real hyperspectral remote sensing images. The experiments show an increase in both stability and performance.

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Section: Entropy-weighted Ensemble Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TRP-Oriented Hyperspectral Remote Sensing Image Classification Using Entropy-Weighted Ensemble Algorithm

Jia

Zhao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The hyperspectral reflectance curves for each class have similar patterns and they can be used as a time series depend on the number of spectral bands used in the imaging system. The constant spectral band difference between the neighboring elements of each reconstructed vector, which is the embedding dimension of the phase space, is represented by the spectral band for each pixel [10]. The phase-space vector of the spectral signal is reconstructed as follows:…”

Section: Chaotic Structure Analysis Using Lyapunov Exponentsmentioning

confidence: 99%