Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

Gao, Lianru; Zhao, Bin; Jia, Xiuping; Liao, Wenzhi; Zhang, Bing

doi:10.3390/rs9060548

Cited by 56 publications

(41 citation statements)

References 59 publications

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“…However, MNF is unable to achieve reliable performances in real applications (Gao et al 2013b). Some work has reported that the fundamental reason constraining the transformation of MNF was inaccuracy in the calculation of NF (Greco 2006;Liu et al 2009;Gao et al 2011;Zhao, Gao and Zhang, 2016;Gao et al 2017). The information content for a particular hyperspectral image remains unchanged; the calculation accuracy of NF mainly depends on the noise estimation results.…”

Section: Open Accessmentioning

confidence: 99%

A new kernel method for hyperspectral image feature extraction

Zhao

Gao

Liao

et al. 2017

Geo-spatial Information Science

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Key laboratory of Digital earth science, institute of remote sensing and Digital earth, chinese academy of sciences, Beijing, china; b University of chinese academy of sciences, ringgold standard institution, Beijing, china; c Department of telecommunications and information processing, Ghent University, Ghent, Belgium ABSTRACTHyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for postapplications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where highdimensional data analysis is required.

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Section: Open Accessmentioning

confidence: 99%

A new kernel method for hyperspectral image feature extraction

Zhao

Gao

Liao

et al. 2017

Geo-spatial Information Science

Self Cite

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“…Hyperspectral sensors have attracted much interest in remote sensing for providing abundant and valuable information over the last few decades. With the useful information, HSI has played a vital role in many applications, among which classification [5][6][7] is one of the crucial processing steps that has received enormous attention. The foremost task in hyperspectral classification is to train an effective classifier with the given training set from each class.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

He¹,

Liu²,

Wang³

et al. 2017

Remote Sensing

140

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Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method, especially with a limited number of labeled samples.

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“…Therefore, hyperspectral images require preprocessing to reduce spectral bands [3,4] and denoising prior to further processing. Many techniques have been applied in hyperspectral data analysis to reduce data dimensionality, including selection-based [5] and transformation-based techniques [6] such as Independent Component Analysis (ICA).…”

Section: Introductionmentioning

confidence: 99%

DCT-Based Preprocessing Approach for ICA in Hyperspectral Data Analysis

Boukhechba

Bazine

2018

Sensors

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The huge quantity of information and the high spectral resolution of hyperspectral imagery present a challenge when performing traditional processing techniques such as classification. Dimensionality and noise reduction improves both efficiency and accuracy, while retaining essential information. Among the many dimensionality reduction methods, Independent Component Analysis (ICA) is one of the most popular techniques. However, ICA is computationally costly, and given the absence of specific criteria for component selection, constrains its application in high-dimension data analysis. To overcome this limitation, we propose a novel approach that applies Discrete Cosine Transform (DCT) as preprocessing for ICA. Our method exploits the unique capacity of DCT to pack signal energy in few low-frequency coefficients, thus reducing noise and computation time. Subsequently, ICA is applied on this reduced data to make the output components as independent as possible for subsequent hyperspectral classification. To evaluate this novel approach, the reduced data using (1) ICA without preprocessing; (2) ICA with the commonly used preprocessing techniques which is Principal Component Analysis (PCA); and (3) ICA with DCT preprocessing are tested with Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) classifiers on two real hyperspectral datasets. Experimental results in both instances indicate that data after our proposed DCT preprocessing method combined with ICA yields superior hyperspectral classification accuracy.

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Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

Cited by 56 publications

References 59 publications

A new kernel method for hyperspectral image feature extraction

A new kernel method for hyperspectral image feature extraction

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

DCT-Based Preprocessing Approach for ICA in Hyperspectral Data Analysis

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