Gustavo Camps-Valls

Research Scientist

Alumni

Empirical Inference

Publications

4 results (View BibTeX file of all listed publications)

2010

Spatio-Spectral Remote Sensing Image Classification With Graph Kernels

Camps-Valls, G., Shervashidze, N., Borgwardt, K.

IEEE Geoscience and Remote Sensing Letters, 7(4):741-745, October 2010 (article)

Abstract

This letter presents a graph kernel for spatio-spectral remote sensing image classification with support vector machines (SVMs). The method considers higher order relations in the neighborhood (beyond pairwise spatial relations) to iteratively compute a kernel matrix for SVM learning. The proposed kernel is easy to compute and constitutes a powerful alternative to existing approaches. The capabilities of the method are illustrated in several multi- and hyperspectral remote sensing images acquired over both urban and agricultural areas.

Web DOI [BibTex]

2010

ei Camps-Valls, G., Shervashidze, N., Borgwardt, K. Spatio-Spectral Remote Sensing Image Classification With Graph Kernels IEEE Geoscience and Remote Sensing Letters, 7(4):741-745, October 2010 (article)

Web DOI [BibTex]

Semi-supervised Remote Sensing Image Classification via Maximum Entropy

Erkan, A., Camps-Valls, G., Altun, Y.

In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), pages: 313-318, IEEE, Piscataway, NJ, USA, 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 2010 (inproceedings)

Abstract

Remote sensing image segmentation requires multi-category classification typically with limited number of labeled training samples. While semi-supervised learning (SSL) has emerged as a sub-field of machine learning to tackle the scarcity of labeled samples, most SSL algorithms to date have had trade-offs in terms of scalability and/or applicability to multi-categorical data. In this paper, we evaluate semi-supervised logistic regression (SLR), a recent information theoretic semi-supervised algorithm, for remote sensing image classification problems. SLR is a probabilistic discriminative classifier and a specific instance of the generalized maximum entropy framework with a convex loss function. Moreover, the method is inherently multi-class and easy to implement. These characteristics make SLR a strong alternative to the widely used semi-supervised variants of SVM for the segmentation of remote sensing images. We demonstrate the competitiveness of SLR in multispectral, hyperspectral and radar image classifica tion.

PDF Web DOI [BibTex]

ei Erkan, A., Camps-Valls, G., Altun, Y. Semi-supervised Remote Sensing Image Classification via Maximum Entropy In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), pages: 313-318, IEEE, Piscataway, NJ, USA, 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 2010 (inproceedings)

PDF Web DOI [BibTex]

Remote Sensing Feature Selection by Kernel Dependence Estimation

Camps-Valls, G., Mooij, J., Schölkopf, B.

IEEE Geoscience and Remote Sensing Letters, 7(3):587-591, July 2010 (article)

Abstract

This letter introduces a nonlinear measure of independence between random variables for remote sensing supervised feature selection. The so-called HilbertSchmidt independence criterion (HSIC) is a kernel method for evaluating statistical dependence and it is based on computing the HilbertSchmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is easy to compute and has good theoretical and practical properties. Rather than using this estimate for maximizing the dependence between the selected features and the class labels, we propose the more sensitive criterion of minimizing the associated HSIC p-value. Results in multispectral, hyperspectral, and SAR data feature selection for classification show the good performance of the proposed approach.

PDF Web DOI [BibTex]

ei Camps-Valls, G., Mooij, J., Schölkopf, B. Remote Sensing Feature Selection by Kernel Dependence Estimation IEEE Geoscience and Remote Sensing Letters, 7(3):587-591, July 2010 (article)

PDF Web DOI [BibTex]

2006

Semi-supervised Hyperspectral Image Classification with Graphs

Bandos, T., Zhou, D., Camps-Valls, G.

In IGARSS 2006, pages: 3883-3886, IEEE Computer Society, Los Alamitos, CA, USA, IEEE International Conference on Geoscience and Remote Sensing, August 2006 (inproceedings)

Abstract

This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the images through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.

PDF Web DOI [BibTex]

2006

ei Bandos, T., Zhou, D., Camps-Valls, G. Semi-supervised Hyperspectral Image Classification with Graphs In IGARSS 2006, pages: 3883-3886, IEEE Computer Society, Los Alamitos, CA, USA, IEEE International Conference on Geoscience and Remote Sensing, August 2006 (inproceedings)

PDF Web DOI [BibTex]

Gustavo Camps-Valls

2010

Spatio-Spectral Remote Sensing Image Classification With Graph Kernels

2010

Semi-supervised Remote Sensing Image Classification via Maximum Entropy

Remote Sensing Feature Selection by Kernel Dependence Estimation

2006

Semi-supervised Hyperspectral Image Classification with Graphs

2006

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