Semisupervised Image Classification With Laplacian Support Vector Machines

Gómez-Chova, L.; Camps‐Valls, Gustau; Muñoz-Marí, Jordi; Calpe, J.

doi:10.1109/lgrs.2008.916070

Cited by 244 publications

(104 citation statements)

References 10 publications

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“…Transductive SVM (TSVM) [4,40], which maximizes the margin for labeled and unlabeled samples simultaneously; 2) Graph-based methods, in which each pixel spreads its label information to its neighbors until a global steady state is achieved on the whole image [41,42]; and 3) the Laplacian SVM (LapSVM) [43,44], which deforms the kernel matrix of a standard SVM with the relations found by building the graph Laplacian. Also, the design of cluster and bagged kernels [37] have been successfully presented in remote sensing [45,46], whose essential idea is to modify the eigenspectrum of the kernel matrix that in turn implies an alteration of the distance metric.…”

Section: Semisupervised Image Classificationmentioning

confidence: 99%

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A Review of Kernel Methods in Remote Sensing Data Analysis

Gómez-Chova

Muñoz-Marí

Laparra

et al. 2011

Optical Remote Sensing

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Summary. Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastructures, agriculture inventorying, disaster prevention and damage assessment, anomaly and target detection, biophysical parameter estimation, band selection, and feature extraction.This chapter provides a survey of applications and recent theoretical developments of kernel methods in the context of remote sensing data analysis. The specific methods developed in the fields of supervised classification, semisupervised classification, target detection, model inversion, and nonlinear feature extraction are revised both theoretically and through experimental (illustrative) examples. The emergent fields of transfer, active, and structured learning, along with efficient parallel implementations of kernel machines, are also revised.

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Section: Semisupervised Image Classificationmentioning

confidence: 99%

“…More details can be found in [43], and its application to remote sensing data classification in [44].…”

Section: Laplacian Support Vector Machine (Lapsvm)mentioning

confidence: 99%

A Review of Kernel Methods in Remote Sensing Data Analysis

Gómez-Chova

Muñoz-Marí

Laparra

et al. 2011

Optical Remote Sensing

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“…In our case, the nodes of the graph are the features, here the voxels, whereas in [18], the nodes were the objects to classify. Laplacian regularization was also used in satellite imaging [19] but, again, the nodes were the objects to classify. Our approach can also be considered as a spectral regularization on the graph [20].…”

Section: Regularization Based On Diffusion On Graphmentioning

confidence: 99%

Spatially Regularized SVM for the Detection of Brain Areas Associated with Stroke Outcome

Cuingnet

Rosso

Lehericy

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. This paper introduces a new method to detect group differences in brain images based on spatially regularized support vector machines (SVM). First, we propose to spatially regularize the SVM using a graph encoding the voxels' proximity. Two examples of regularization graphs are provided. Significant differences between two populations are detected using statistical tests on the margins of the SVM. We first tested our method on synthetic examples. We then applied it to 72 stroke patients to detect brain areas associated with motor outcome at 90 days, based on diffusion-weighted images acquired at the acute stage (one day delay). The proposed method showed that poor motor outcome is associated to changes in the corticospinal bundle and white matter tracts originating from the premotor cortex. Standard mass univariate analyses failed to detect any difference.

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“…A wide variety of SVM's modifications have been proposed to improve its performance. Some of them incorporate the contextual information in the classifiers [2], [3]. Others design sparse SVM in order to pursue a sparse decision rule by using ℓ 1 -norm as the regularizer [4].…”

Section: Introductionmentioning

confidence: 99%

Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

Sun

Nasrabadi

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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Abstract-Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results compared with that of traditional classifiers such as the support vector machine (SVM). Recently, by incorporating additional structured sparsity priors, the second generation SRCs have appeared in the literature and are reported to further improve the performance of HSI. These priors are based on exploiting the spatial dependencies between the neighboring pixels, the inherent structure of the dictionary, or both. In this paper, we review and compare several structured priors for sparserepresentation-based HSI classification. We also propose a new structured prior called the low rank group prior, which can be considered as a modification of the low rank prior. Furthermore, we will investigate how different structured priors improve the result for the HSI classification.

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Semisupervised Image Classification With Laplacian Support Vector Machines

Cited by 244 publications

References 10 publications

A Review of Kernel Methods in Remote Sensing Data Analysis

A Review of Kernel Methods in Remote Sensing Data Analysis

Spatially Regularized SVM for the Detection of Brain Areas Associated with Stroke Outcome

Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

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