Remote sensing applications

Randomized Kernels for Large Scale Earth Observation Applications

Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models or the classification of high spatial-spectral-temporal resolution data. This paper introduces an efficient kernel method for fast statistical retrieval of atmospheric and biophysical parameters and image classification problems real applications. We rely on a recently presented approximation to shift-invariant kernels using projections on random Fourier features. The method allows to approximate a kernel matrix with a set of projections on random bases sampled from the Fourier domain. The method is simple, computationally very efficient in both memory and processing costs, and easily parallelizable.

MERIS/AATSR Synergy Cloud Screening

A module for the BEAM plaftorm that provides cloud screening within the MERIS/AATSR Synergy Toolbox. The MERIS/AATSR Synergy Toolbox provides processing schemes for improved cloud screening, global aerosol retrieval and land atmospheric correction using the combined multi-spectral and multi-angle information from geo-located and geo-registered MERIS and AATSR measurements.

SIMFEAT: A simple MATLAB(tm) toolbox of linear and kernel feature extraction

Toolbox of linear and kernel feature extraction: (1) Linear methods: PCA, MNF, CCA, PLS, OPLS, and (2) Kernel feature extractors: 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, J. and Petersen, K.B. and Camps-Valls, G. and Hansen, L.K. IEEE Signal Processing Magazine 30 (4):16-29, 2013
  • 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), aka Least Squares SVM, Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VHGPR). We also include a dataset of collected spectra and associated chlorophyll content to illustrate the training/testing procedures. This is just a demo providing a default initialization. Training is not at all optimized. Other initializations, optimization techniques, and training strategies may be of course better suited to achieve improved results in this or other problems. We just did it in the standard way for illustration and educational purposes, as well as to disseminate these models.

    References
  • Retrieval of biophysical parameters with heteroscedastic Gaussian processes Lázaro-Gredilla, M. and Titsias, M.K. and 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. and Casanova-Mateo, C. and 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, simpleUnmix, contains a set of functions in Matlab to illustrate the capabilities of the most representative methods in a generic process of spectral unmxing: endmember determination methods, spectral unmixing, and abundance estimation.

    References
  • Remote Sensing Image Processing Camps-Valls, G. and Tuia, D. and Gómez-Chova, L. and Jiménez, S. and Malo, J. Collection "Synthesis Lectures on Image, Video, and Multimedia Processing" Al Bovik, Ed. Morgan \& Claypool Publishers, LaPorte, CO, USA, 2011
  • simpleClass: Simple Classification Toolbox

    A set of train-test simple educational functions for data classification: LDA, QDA, Mahalanobis-distance classifier, decision trees, random forests, SVM, Boosting, Bagging, Gaussian process classifiers, etc.

    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. and Tuia, D. and Camps-Valls, G. IEEE Transactions on Geoscience and Remote Sensing 50 (10 PART1):3751-3763, 2012
  • Remote sensing image segmentation by active queries Tuia, D. and Muñoz-Marí, J. and Camps-Valls, G. Pattern Recognition 45 (6):2180-2192, 2012
  • ARTMO: Automated Radiative Transfer Models Operator

    The in-house developed Automated Radiative Transfer Models Operator (ARTMO) Graphic User Interface (GUI) is a software package that provides essential tools for running and inverting a suite of plant RTMs, both at the leaf and at the canopy level. ARTMO facilitates consistent and intuitive user interaction, thereby streamlining model setup, running, storing and spectra output plotting for any kind of optical sensor operating in the visible, near-infrared and shortwave infrared range (400-2500 nm). the ARTMO package includes physical, statistical and hybrid inversion and model emulation. Some modules are pure machine learning techniques for regression, active learning, dimensionality reduction and feature ranking!

    References
  • Toward a semiautomatic machine learning retrieval of biophysical parameters Caicedo, J.P.R. and Verrelst, J. and Munoz-Mari, J. and Moreno, J. and Camps-Valls, G. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (4):1249-1259, 2014