IEEE Geoscience and Remote Sensing Letters, 7(4):741-745, October 2010 (Article)
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.
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 (Conference Paper)
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
IEEE Geoscience and Remote Sensing Letters, 7(3):587-591, July 2010 (Article)
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.
In Proceedings of the IEEE International Conference on Geoscience and Remote Sensing (IGARSS 2006), IGARSS 2006, pages: 3883-3886, IEEE Computer Society, Los Alamitos, CA, USA, IEEE International Conference on Geoscience and Remote Sensing, August 2006 (Conference Paper)
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.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems