A Spatial–Contextual Support Vector Machine for Remotely Sensed Image Classification

Li, Cheng‐Hsuan; Kuo, Bor‐Chen; Lin, Chin‐Teng; Huang, Chih-Fang

doi:10.1109/tgrs.2011.2162246

Cited by 97 publications

(28 citation statements)

References 32 publications

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“…We illustrate three-class synthetic data (here, three classes are chosen from the University of Pavia data that will be introduced in Section 4) to demonstrate the sensitivity of these two parameters. The typical support vector machine (SVM) [32,33] is employed to measure the classification accuracy. The signal-to-noise ratio (SNR) of 20 dB and 30 dB Gaussian noise [34] and infinite (here, Inf means that no additional noise is used) is simulated.…”

Section: Analysis On Gda-ssmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

Feng

et al. 2017

Remote Sensing

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Abstract:Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS) measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA).

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Section: Analysis On Gda-ssmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

Feng

et al. 2017

Remote Sensing

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“…In [41], Baassou et al combined the dispersion of spatial and spectral information to carry out high spectral image classification. Support vector machine (SVM) [7,[42][43][44][45][46] is a typical kernel-based classification method; the basic idea is to map the originally indivisible feature space to the high-dimensional linear separable feature space by kernel function, so as to solve a non-linear classification problem by a linear classification method. Since the dimension of the original data has no effect on the size of the kernel matrix, the kernel function method can effectively deal with the high-dimensional data, thus avoiding the dimension disaster problem of the traditional pattern recognition methods.…”

Section: Related Workmentioning

confidence: 99%

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

2018

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When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc.) among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework.

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“…Computer vision includes many applications of SVM like person identification [23], hand gesture detection [24], face recognition [25] and background subtraction [26]. In geosceinces, SVM have been applied to remote sensing analysis [27][28][29], land cover change [30][31][32], landslide susceptibility [33][34][35][36] and hydrology [37,38]. In power systems, SVM was used for transient status prediction [39], power load forecasting [40], electricity consumption prediction [41] and wind power forecasting [42].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Support Vector Machines with Uncertainties

Wang

Pardalos

2014

Ann. Data. Sci.

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Support Vector Machines (SVM) is one of the well known supervised classes of learning algorithms. SVM have wide applications to many fields in recent years and also many algorithmic and modeling variations. Basic SVM models are dealing with the situation where the exact values of the data points are known. This paper presents a survey of SVM when the data points are uncertain. When a direct model cannot guarantee a generally good performance on the uncertainty set, robust optimization is introduced to deal with the worst case scenario and still guarantee an optimal performance. The data uncertainty could be an additive noise which is bounded by norm, where some efficient linear programming models are presented under certain conditions; or could be intervals with support and extremum values; or a more general case of polyhedral uncertainties with formulations presented. Another field of the uncertainty analysis is chance constrained SVM which is used to ensure the small probability of misclassification for the uncertain data. The multivariate Chebyshev inequality and Bernstein bounding schemes have been used to transform the chance constraints through robust optimization. The Chebyshev based model employs moment information of the uncertain training points. The Bernstein bounds can be less conservative than the Chebyshev bounds since it employs both support and moment information, but it also makes a strong assumption that all the elements in the data set are independent.

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A Spatial–Contextual Support Vector Machine for Remotely Sensed Image Classification

Cited by 97 publications

References 32 publications

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

A Survey of Support Vector Machines with Uncertainties

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