ALTB is a set of tools implementing state-of-the-art active learning algorithms for remote sensing applications.

A simple Toolbox for Anomaly Change Detection (ACD) with Gaussianity assumptions and Elliptically Contoured (EC) distributions.

A graph kernel for spatio-spectral remote sensing image classification with support vector machines (SVM). 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.

A graph-based method for semi-supervised learning: essentially an affinity matrix is computed, the graph Laplacian is normalized, and a spreading function is iterated until convergence. This algorithm can be understood intuitively in terms of spreading activation networks from experimental psychology, and explained as random walks on graphs. We successfully apply it to hyperspectral image classification. It incorporates contextual information through a full family of composite kernels. Noting that the graph method relies on inverting a huge kernel matrix formed by both labeled and unlabeled samples, we originally introduce the Nyström method in the formulation to speed up the classification process.

A semi-supervised SVM for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.

A (large margin) SVM algorithm that learns convolutional filters, with applications to time series analysis and remote sensing image classification.

This code+data snippet implements the automatic change detection algorithm presented in the SPIE 2010 and IGARSS 2011 papers. It consists in three steps: initialization (histogram-based), automatic parameter tuning and change map generation, with classical kmeans, gaussian kernel kmeans and by clustering the difference image in the feature spaces. Loops allows to perform different experiments and the user can choose different parameters (number of experiments, kernel function and parameters to search, number of pseudo training samples,...). Moreover, one can easily adapt the code for comparisons to other algorithms and images, as well as adapting the code for personal developments.