Remote sensing applications
ALTB: Active Learning MATLAB(tm) Toolbox
ALTB is a set of tools implementing state-of-the-art active learning algorithms for remote sensing applications.
References
- Semisupervised classification of remote sensing images with active queries. Munoz-Mari, J., Tuia, D., and Camps-Valls, G. IEEE Transactions on Geoscience and Remote Sensing 50(10): 3751-3763, 2012.
- Remote sensing image segmentation by active queries. Tuia, D., Muñoz-Marí, J., Camps-Valls, G. Pattern Recognition 45(6): 2180-2192, 2012.
Kernel Vegetation Indices
Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere. Here we generalize the family of commonly used vegetation indices by exploiting all higher-order relations between spectral channels. This results in higher sensitivity to vegetation biophysical and physiological parameters, improving the monitoring of key parameters such as leaf area index and gross primary productivity.
References
- A Unified Vegetation Index for Quantifying the Terrestrial Biosphere. Gustau Camps-Valls et al., Science Advances, Vol. 7, no. 9, eabc7447, 2021.
- Science Advances (open access, no hyperlinks)
- Preprint paper+supplementary material in one file (with hyperlinks)
MERIS/AATSR Synergy Cloud Screening
A module for the BEAM platform that provides cloud screening within the MERIS/AATSR Synergy Toolbox. This toolbox offers processing schemes for improved cloud screening, aerosol retrieval, and land atmospheric correction using combined multi-spectral and multi-angle information from MERIS and AATSR measurements.
Randomized Kernels for Large Scale Earth Observation Applications
Kernel methods are powerful machine learning algorithms, widely used in remote sensing and geosciences. This paper introduces an efficient kernel method for fast statistical retrieval of atmospheric and biophysical parameters. The method approximates a kernel matrix with projections on random bases sampled from the Fourier domain.
SIMFEAT: A simple MATLAB(tm) toolbox of linear and kernel feature extraction
Toolbox for linear and kernel feature extraction, including PCA, MNF, CCA, PLS, OPLS, and kernel methods like KPCA, KMNF, KCCA, KPLS, KOPLS, and KECA.
References
- Kernel multivariate analysis framework for supervised subspace learning: A tutorial on linear and kernel multivariate methods. Arenas-Garcia et al., IEEE Signal Processing Magazine, 30(4):16-29, 2013.
simpleClass: Simple Classification Toolbox
A set of train-test simple educational functions for data classification, including methods like LDA, QDA, decision trees, random forests, SVM, Boosting, and Gaussian process classifiers.
simpleR v2.1: simple Regression toolbox
The simple Regression toolbox, simpleR, contains a set of functions in Matlab to illustrate the capabilities of several statistical regression algorithms. simpleR contains simple educational code for linear regression (LR), decision trees (TREE), neural networks (NN), support vector regression (SVR), kernel ridge regression (KRR), Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VHGPR). A dataset of spectra and associated chlorophyll content is included to illustrate training/testing procedures.
References
- Retrieval of biophysical parameters with heteroscedastic Gaussian processes. Lázaro-Gredilla, M., Titsias, M.K., Verrelst, J., and Camps-Valls, G. IEEE Geoscience and Remote Sensing Letters, 11(4):838-842, 2014.
- Prediction of daily global solar irradiation using temporal Gaussian processes. Salcedo-Sanz, S., Casanova-Mateo, C., Muñoz-Marí, J., and Camps-Valls, G. IEEE Geoscience and Remote Sensing Letters, 11(11):1936-1940, 2014.
simpleUnmix: simple Unmixing and Abundance estimation toolbox
The simple Unmixing toolbox contains a set of Matlab functions for spectral unmixing, including endmember determination methods, spectral unmixing, and abundance estimation.
References
- Remote Sensing Image Processing. Camps-Valls, G. et al., Synthesis Lectures on Image, Video, and Multimedia Processing, Morgan & Claypool Publishers, 2011.