A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters for hyperspectral data

Li, Na; Zhao, Haitao; Huang, Ping; Jia, Guorui

doi:10.1080/00207160.2014.915957

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…More precisely, Combrexelle et al [30] calculated coefficients of the polynomial describing the multifractal spectrum for each spectral band. Li et al in [42] showed that using multifractal parameters for spectral profiles' description may improve the average accuracy by 7-8%. Incorporation of multifractal parameters resulted in higher overall accuracy (almost 10%) in the algorithm proposed by Wan et al [43].…”

Section: Multifractalsmentioning

confidence: 99%

What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

et al. 2020

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Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis methods. Therefore, this research applies, for the first time, the degree of multifractality to the global description of all spectral bands of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Subsets of four hyperspectral images, presenting four landscape types, are analysed. In particular, we verify whether multifractality can be detected in all spectral bands. Furthermore, we analyse variability in multifractality as a function of wavelength, for data before and after atmospheric correction. We try to identify absorption bands and discuss whether multifractal parameters provide additional value or can help in the problem of dimensionality reduction in hyperspectral data or landscape type classification.

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Section: Multifractalsmentioning

confidence: 99%

What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

et al. 2020

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“…Due to the powerful data acquisition and analysis capabilities, the hyperspectral technique is naturally applied in intelligent agriculture [3][4][5][6][7][8][9]. Nevertheless, since that the hyperspectral remote sensing has the characteristics of multi band, narrow band width and large amount of data, the analysis of dynamic structure of hyperspectral signal become a critical premise to promote its effective utilization [10][11][12]. One of the key pretreatment task is to detect information redundancy hidden in hyperspectral, which seriously affects the extraction of real information of spectrum [13].…”

Section: Introductionmentioning

confidence: 99%

A New Hyperspectral Redundant Band Detection Method Based on Local Hurst Exponent

Li¹,

Li²,

Wang³

et al. 2021

Preprint

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Hyperspectrum reflectance is a curve in a certain wavelength range. Its complex dynamic structure reflects rich information of the object at variable bands. However, the potential redundancy will seriously affect accurate extraction of spectral features, therefore, information redundancy detection is a critical pretreatment for spectral analysis. In this paper, by using the local detrended fluctuation analysis, we propose a new method to detect the redundant bands. The method focuses on the spectral auto-correlation represented by local Hurst exponent in moving windows. Thus, the redundant band can be determined by the comparison of auto-correlation between two adjacent windows. To test our method, using the fractal feature of the removing redundant bands as augment, rapeseed oleic acid's prediction model is constructed based on random decision forest method. As comparison, the same feature of the original spectrum is also employed as augment for the model. The result shows that the feature of removing the redundant bands will bring better model performance than the feature of original spectrum does.

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“…A very challenging problem in the remote sensing community is to generate land-cover maps for semantically characterizing Earth's surface [1,2,3,4,5,6]. As one of the most widely used approaches, hyperspectral image (HSI) classification has recently gained in popularity and attracted research interests from other scientific disciplines such as image processing, machine learning, and computer vision [7,8,9,10,11,12,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Active multi-kernel domain adaptation for hyperspectral image classification

Deng

Liu

et al. 2018

Pattern Recognition

127

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Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains. To address this issues, in this paper we show a novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL). The main idea of our method is to retrain the multi-kernel classifier by utilizing the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain. The proposed method adaptively combines multiple kernels, forming a DA classifier that minimizes the bias between the source and target domains. Further equipped with the nested actively updating process, it sequentially expands the training set and gradually converges to a satisfying level of classification performance. We study this active adaptation framework with the Margin Sampling (MS) strategy in the HSI classification task. Our experimental results on two popular HSI datasets demonstrate its effectiveness.

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A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters for hyperspectral data

Cited by 11 publications

References 26 publications

What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

A New Hyperspectral Redundant Band Detection Method Based on Local Hurst Exponent

Active multi-kernel domain adaptation for hyperspectral image classification

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