Using class-based feature selection for the classification of hyperspectral data

Maghsoudi, Yasser; Zoej, Mohammad Javad Valadan; Collins, Michael

doi:10.1080/01431161.2010.486416

Cited by 27 publications

(17 citation statements)

References 36 publications

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“…In these studies fusion is performed on the classifiers' soft outputs (either probabilistic or fuzzy), through simple weighted averaging schemes [20,21,24], more complex fusion operators [22], or even by considering the stacked (soft) outputs of the multiple classifiers as a new feature space and subsequently training a new classifier on this new space [23]. Decision fusion has also been exploited for effectively tackling the very high dimensionality of hyperspectral data, by training multiple classifiers on different feature subsets derived from the source image and then combining their outputs [25][26][27][28][29]. The feature subsets are derived either through some appropriate feature extraction algorithm [25] or-more commonly-by applying some feature subgroups selection process [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Decision fusion has also been exploited for effectively tackling the very high dimensionality of hyperspectral data, by training multiple classifiers on different feature subsets derived from the source image and then combining their outputs [25][26][27][28][29]. The feature subsets are derived either through some appropriate feature extraction algorithm [25] or-more commonly-by applying some feature subgroups selection process [26][27][28][29]. Α more direct decision fusion approach combines the outputs of multiple different classifiers applied to a common data source [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

et al. 2014

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This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral's spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

et al. 2014

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“…[11]. Maghsoudi and others proposed a class-based schema for the band selection and classification of hyperspectral images [12].…”

Section: A Overseas Research Status Of Band Selection For Hyperspectmentioning

confidence: 99%

Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images

Wang

Huang

Liu

et al. 2012

2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering

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The typical characteristics of hyperspectral remote sensing data are combining image with spectrum, high spectral resolution, many bands and much redundant information. A hyperspectral image cube can be used to obtain millions spectrum curves. Aiming at a hyperspectral remote sensing image containing huge amounts of data, removing redundant information and reducing processing dimensions are the premise and foundation for hyperspectral remote sensing processing and applications. Hyperspectral data dimension reduction techniques mainly include the feature extraction and band selection. This paper has fully studied the theories and methods of dimension reduction for band selection, analyses their advantages, disadvantages and validity, and deeply discusses the current situation and tendency of the development of band selection based on dimension reduction of hyperspectral remote sensing image at last.

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“…Consequently, the further improvement in classification accuracy for subsequent classification will be hindered. Several studies have pointed out that the feature subset that can best describe the discriminants may vary for different pairs of classes [27][28][29]. Therefore, calculating for each pair, the class separability value might increase the scattering extent of selected features in the process of maximizing the averaged pairwise class separability value.…”

Section: Scatter-matrix-based Feature Selectionmentioning

confidence: 99%

An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images

Zhao

Wang

et al. 2017

Remote Sensing

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Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral

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Using class-based feature selection for the classification of hyperspectral data

Cited by 27 publications

References 36 publications

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images

An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images

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