Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

Lunga, Dalton; Prasad, Saurabh; Crawford, Melba M.; Ersoy, Okan K.

doi:10.1109/msp.2013.2279894

Cited by 256 publications

(144 citation statements)

References 43 publications

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“…where N t represents a set of triples (i, j, l) ∈ N t if and only if (i, j, l) triggers the hinge loss in (4). Finally, a sample x i in the lower dimensional feature space can be represented as…”

Section: A Lmnnmentioning

confidence: 99%

“…The high dimensionality problem is addressed through feature reduction strategies by projecting the original data into a lower dimensional feature space prior to classification. Commonly used feature extraction techniques include both linear [3] and nonlinear approaches [4]. Active learning (AL) has also been demonstrated to be an effective approach for dealing with the limited availability of labeled samples [5].…”

Section: Introductionmentioning

confidence: 99%

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Multimetric Active Learning for Classification of Remote Sensing Data

Zhang

Pasolli

Yang

et al. 2016

IEEE Geosci. Remote Sensing Lett.

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The classification of hyperspectral and multimodal remote sensing data is affected by two key problems: the high dimensionality of the input data and the limited number of the labeled samples. In this letter, a multimetric learning approach that combines feature extraction and active learning (AL) is introduced to deal with these two issues simultaneously. In particular, distinct metrics are assigned to different types of features and then learned jointly. In this way, multiple features are projected into a common feature space, in which AL is then performed in conjunction with k-nearest neighbor classification to enrich the set of labeled samples. Experiments on two sets of remote sensing data illustrate the effectiveness of the proposed framework in terms of both classification accuracy and computational requirements.

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Section: A Lmnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimetric Active Learning for Classification of Remote Sensing Data

Zhang

Pasolli

Yang

et al. 2016

IEEE Geosci. Remote Sensing Lett.

Self Cite

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“…The first one is supervised learning for HSIs, which is generally called classification [7][8][9]. HSI classification is usually limited to the number of labeled samples, since it is time-consuming to collect large numbers of training samples [10][11][12]. The second category is unsupervised learning named clustering, which does not need to label a huge volume of training samples.…”

Section: Introductionmentioning

confidence: 99%

Class Probability Propagation of Supervised Information Based on Sparse Subspace Clustering for Hyperspectral Images

et al. 2017

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Hyperspectral image (HSI) clustering has drawn increasing attention due to its challenging work with respect to the curse of dimensionality. In this paper, we propose a novel class probability propagation of supervised information based on sparse subspace clustering (CPPSSC) algorithm for HSI clustering. Firstly, we estimate the class probability of unlabeled samples by way of partial known supervised information, which can be addressed by sparse representation-based classification (SRC). Then, we incorporate the class probability into the traditional sparse subspace clustering (SSC) model to obtain a more accurate sparse representation coefficient matrix accompanied by obvious block diagonalization, which will be used to build the similarity matrix. Finally, the cluster results can be obtained by applying the spectral clustering on similarity matrix. Extensive experiments on a variety of challenging data sets illustrate that our proposed method is effective.

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“…Instances of these applications include projecting remote sensing data into a low dimensional space for visualization or extracting properties for remote sensing data classification. A number of exploratory studies [2,10,11,12] have shown that manifold learning algorithms can be used to analysis and classify remote sensing data successfully in a low dimensionality space.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Data Classification Using Combined Spectral and Spatial Local Linear Embedding (CSSLE)

Xue¹,

Yi²,

Liu³

et al. 2017

dtcse

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Abstract. Feature learning and extraction have been used to reduce the complexity of the representation of remote sensing data. In this paper, a novel remote sensing data feature analysis method is proposed based on the locally linear embedding (LLE) techniques, which is an unsupervised manifold learning algorithm. If the remote sensing data variability is described by a small number of spatial features, we can treat the data as lying on a low dimensional manifold in the high dimensional space of remote sensing data. The proposed method, combined spectral and spatial local linear embedding (CSSLE) makes use the neighbors in both domains. Compared with LLE, CSSLE makes up the shortage that LLE ignores the relationship among the spatial neighboring pixels which is extremely important for remote sensing images. In this paper we provide experiment results from the analysis of remote sensing data using PCA, LLE and CSSLE. Classification results show that proposed method can give higher accuracies than the linear method of PCA and the nonlinear method of LLE.

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Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

Cited by 256 publications

References 43 publications

Multimetric Active Learning for Classification of Remote Sensing Data

Multimetric Active Learning for Classification of Remote Sensing Data

Class Probability Propagation of Supervised Information Based on Sparse Subspace Clustering for Hyperspectral Images

Remote Sensing Data Classification Using Combined Spectral and Spatial Local Linear Embedding (CSSLE)

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