Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks

Jiménez, Luís Ortiz; Morales-Morell, A.; Creus, A.

doi:10.1109/36.763300

Cited by 89 publications

(24 citation statements)

References 12 publications

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“…In recent years, there has also been growing interest in fusing several properties (e.g., texture and spectral information) of single data source (termed feature fusion) or certain properties of multidata sources (SAR and optical sensors) under centralized decision (termed decision fusion) to improve classification accuracy [29]- [31]. Feature fusion schemes retain the best features to improve the classification accuracy locally, while decision fusion schemes integrate local classifiers to improve the overall performance.…”

Section: Introductionmentioning

confidence: 99%

“…Feature fusion schemes retain the best features to improve the classification accuracy locally, while decision fusion schemes integrate local classifiers to improve the overall performance. Several studies have been published in this regard [29]- [31]. In [29], the dimensions of five different classes were reduced and then used to generate the final decision through majority voting and a neural network.…”

Section: Introductionmentioning

confidence: 99%

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Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Grieve

AlSuwaidi

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Recently, there has been a significant increase in the use of proximal or remote hyperspectral imaging systems to study plant properties, types, and conditions. Numerous financial and environmental benefits of using such systems have been the driving force behind this growth. This paper is concerned with the analysis of hyperspectral data for detecting plant diseases and stress conditions and classifying crop types by means of advanced machine learning techniques. Main contribution of the work lies in the use of an innovative classification framework for the analysis, in which adaptive feature selection, novelty detection, and ensemble learning are integrated. Three hyperspectral datasets and a nonimaging hyperspectral dataset were used in the evaluation of the proposed framework. Experimental results show significant improvements achieved by the proposed method compared to the use of empirical spectral indices and existing classification methods.Index Terms-Ensemble learning, feature selection, hyperspectral imaging (HSI), novelty detection (ND), plant monitoring, remote sensing, support vector machine (SVM).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Grieve

AlSuwaidi

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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%

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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“…Decision-level fusion is a high-level data fusion technique [7,8]. It aims to increase classification accuracy by combining multiple outputs from multiple data sets.…”

Section: Introductionmentioning

confidence: 99%

Multi-Frame Face Classification with Decision-Level Fusion based on Photon-Counting Linear Discriminant Analysis

Yeom

2014

International Journal of Fuzzy Logic and Intelligent Systems

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Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

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Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks

Cited by 89 publications

References 12 publications

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

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

Multi-Frame Face Classification with Decision-Level Fusion based on Photon-Counting Linear Discriminant Analysis

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