Dimensionality Reduction Techniques: An Operational Comparison On Multispectral Satellite Images Using Unsupervised Clustering

Journaux, Ludovic; Tizon, Xavier; Foucherot, Irène; Gouton, Pierre

doi:10.1109/norsig.2006.275233

Cited by 9 publications

(12 citation statements)

References 6 publications

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“…Therefore, we, respectively, adopt the seven RBF and Cauchy kernel functions for Indian Pines and KSC such as their scale parameters in the range [ 0 /8, 8 0 ]. The central parameter ( 0 ) is determined by (15). The SVM is again employed for classification after FE.…”

Section: Comparison With Kpca and A-kpcamentioning

confidence: 99%

“…In this section, we compare our method (M-KPCA) with the five FE methods, i.e., LDA, LPP, pPCA, RP, and t-SNE. M-KPCA is constructed with the subkernels indicated in Table 2, and kernel parameters are determined from (15). Different values of the dimensionality number of the new subspaces are tested for the SVM classifier across the two data sets.…”

Section: M-kpca Versus Other Dimension Reduction Algorithmsmentioning

confidence: 99%

“…Use of PCA for dimensionality reduction in HSI is a computationally suitable approach and it helps preserve the most of the variance of the raw data. Although PCA has some theoretical inadequacies [11,12] for use on remote sensing data, particularly hyperspectral images [13], the practical applications show that the results obtained using PCA are still competitive for the purpose of classification [14,15]. The ability of PCA is limited for high-dimensional data since it relies on only second-order statistical information.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

Binol

2018

Mathematical Problems in Engineering

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Classification is one of the most challenging tasks of remotely sensed data processing, particularly for hyperspectral imaging (HSI). Dimension reduction is widely applied as a preprocessing step for classification; however the reduction of dimension using conventional methods may not always guarantee high classification rate. Principal component analysis (PCA) and its nonlinear version kernel PCA (KPCA) are known as traditional dimension reduction algorithms. In a previous work, a variant of KPCA, denoted as Adaptive KPCA (A-KPCA), is suggested to get robust unsupervised feature representation for HSI. The specified technique employs several KPCAs simultaneously to obtain better feature points from each applied KPCA which includes different candidate kernels. Nevertheless, A-KPCA neglects the influence of subkernels employing an unweighted combination. Furthermore, if there is at least one weak kernel in the set of kernels, the classification performance may be reduced significantly. To address these problems, in this paper we propose an Ensemble Learning (EL) based multiple kernel PCA (M-KPCA) strategy. M-KPCA constructs a weighted combination of kernels with high discriminative ability from a predetermined set of base kernels and then extracts features in an unsupervised fashion. The experiments on two different AVIRIS hyperspectral data sets show that the proposed algorithm can achieve a satisfactory feature extraction performance on real data.

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Section: Comparison With Kpca and A-kpcamentioning

confidence: 99%

Section: M-kpca Versus Other Dimension Reduction Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

Binol

2018

Mathematical Problems in Engineering

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“…Even though its theoretical limitations for hyperspectral data analysis have been pointed out (Landgrebe 2003;Lennon 2002), in a practical situation the results obtained using the PCA are still competitive for the purpose of classification (Journaux et al 2006;Lennon et al 2001). The advantages of the PCA are its low complexity and the absence of parameters.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Machine Learning Techniques in Remote Sensing Data Analysis

Waske

Fauvel

Benediktsson

et al. 2009

Kernel Methods for Remote Sensing Data Analysis

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Several applications have been developed in the field of remote sensing image analysis during the last decades. Besides well-known statistical approaches, many recent methods are based on techniques taken from the field of machine learning. A major aim of machine learning algorithms in remote sensing is supervised classification, which is perhaps the most widely used image classification approach. In this chapter a brief introduction to machine learning and the different paradigms in remote sensing is given. Moreover this chapter briefly discusses the use of recent developments in supervised classification techniques such as neural networks, support vector machines and multiple classifier systems. Introduction Challenges in remote sensingOwing to the recent development of different Earth observation platforms with increased spatial and spectral resolution as well as higher revisit times, remote sensing provides more detailed information on land cover and the environmental state than ever before. Moreover, different Earth-observation systems, such as multi-spectral and SAR systems operate in different wavelengths, ranging from visible to microwave. The data sets consequently provide different, but complementary information. The classification of such data might be considered complex on the one hand, but with regard to recent and upcoming missions, remote sensing applications become even more attractive, on the other.Many early techniques have been taken directly from signal processing and these methods are often based on simple data models and approaches. However, when dealing with recent data sets these well-known classifiers can be limited (Richards 2005). In addition, increased performance requirements such as speed (e.g., for operational monitoring systems and nearreal time applications) and accuracy, further demand the development of more sophisticated analysis concepts (Jain et al. 2000). Thus, the development of adequate methods for various data sets is an important ongoing research topic in the field of remote sensing. This chapter is organized as follows. In Section 1.1.2 a general introduction to machine learning is given, followed by a discussion on different paradigms in remote sensing. In Section 1.2 various supervised classifiers are introduced. A conclusion is given in Section 1.3. General concepts of machine learningMachine Learning is an area of artificial intelligence and generally refers to the development of methods that optimize their performance iteratively by learning from the data. Such methods can be predictive (e.g., a regression model) and make a prediction of a specific phenomenon or descriptive (e.g., a classification model) and distinguish for example between different classes of patterns. In the field of remote sensing, descriptive machine learning algorithms often focus on land cover classifications, and thus provide important input information in several environmental monitoring systems, as for instance in the area of flood forecast, urban sprawl and land degradation. In this cont...

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“…PCA plays an important role in the processing of remote sensing images. Even though its theoretical limitations for hyperspectral data analysis have been pointed out [6,16], in a practical situation, the results obtained using PCA are still competitive for the purpose of classification [17,18]. The advantages of PCA are its low complexity and the absence of parameters.…”

Section: Introductionmentioning

confidence: 99%

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

Fauvel

Chanussot

Benediktsson

2009

EURASIP J. Adv. Signal Process.

222

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Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment, it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experiment, kernel principal components are used to construct the extended morphological profile (EMP). Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principal component analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data. For the one data set, the overall classification accuracy increases from 79% to 96% with the proposed approach.

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Dimensionality Reduction Techniques: An Operational Comparison On Multispectral Satellite Images Using Unsupervised Clustering

Cited by 9 publications

References 6 publications

Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

Machine Learning Techniques in Remote Sensing Data Analysis

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

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