Displacement of human populations due to more extreme weather hazards is a global phenomenon that leads to significant human and economic losses. Mobility related to slow-onset events such as droughts is particularly challenging to model because the start and duration of droughts are uncertain, and their effects are often intertwined with other contextual factors, such as conflicts, political stability, and undependable market prices, that are difficult to measure. Moreover, the collection of in situ socioeconomic data poses a significant challenge, where the use of alternative data sources to warn and plan for impending waves of displacement effectively could help. This study investigates the use of social media as an additional input feature to enhance drought-induced displacement predictions. A methodology for identifying human displacement based on tweet activity is proposed. Results from displacement models based on interpretable machine learning, socioeconomic data, and weather indicators consistently show the benefit of including Twitter data when applied at the district level in Somalia. The proposed approach could enhance drought-induced displacement predictions, thereby helping anticipatory action and the planning of humanitarian interventions.

Food security is at stake, with climate change heavily impacting agriculture and food production. In the present context of extreme events and changing conditions, developing advanced crop yield models can learn from all available information, and providing interpretable predictions for decision-making is of paramount relevance. This work explores the potential and limitations of developing interpretable crop yield models using long short-term memory (LSTM) neural networks, which typically excel at extracting information from time series. LSTMs were designed and trained with multisource satellite and meteorological time series over Continental US (CONUS) and corn, soybean, and wheat yield data from the US Department of Agriculture. Two recent attribution methods are used to interpret and extract knowledge from the developed models: integrated gradients (IG), based on back-propagation, and Shapley (SHAP) values, based on perturbations. Our results show that: 1) LSTM models achieved high accuracy ($\text {R}^{2}>0.56$); 2) multisource combinations outperformed single-variable models in all crop models; 3) both attribution methods were equivalent in detecting essential drivers and their contribution; 4) satellite estimates of enhanced vegetation index (EVI) and vegetation optical depth (VOD) together with meteorological estimates of maximum temperature (TMX) were the most relevant input features for crop yield estimations; and finally and 5) we discovered critical periods of the crop growth cycle for the corn, soybean, and wheat models. The suggested strategy constitutes an important step toward modeling and understanding crop production systems and advancing in automatic data-driven and accountable field management.

Retrieval of physical parameters is of paramount relevance for Earth monitoring. Statistical (machine) learning approaches have been successfully introduced in the community, because they can learn nonlinear functional relations from observational data with no strong a priori assumptions and parametric forms. However, these methods still have two relevant problems: they only consider only one nonlinear feature map of the data, which can be limiting in complex problems where inputs (e.g., radiances) and outputs (e.g., state vectors) have strong nonlinear relations, and in most of the cases, models do not incorporate the structure of the output (dependent) variables. This article proposes a kernel method that solves the two aforementioned problems for physical parameter retrieval: first, it performs multioutput regression with the desired number of connected mappings, and second, it incorporates the output variables structure via a dedicated kernel. The proposed method has a closed-form solution, and thus, neither kernel dimensionality reduction nor preimaging is necessary unlike in previous structured kernel methods. Through the definition of appropriate kernel feature mappings, we also derive a pragmatic deep structured kernel ridge regression (KRR). The method is characterized statistically using a Gaussian process (GP) treatment and providing guarantees based on the concepts of leverage scores (LSs) and effective dimension: both explain that including an output structure acts as a powerful regularizer. We illustrate the method’s performance in toy examples and remote sensing parameter estimation problems involving vegetation parameters [chlorophyll, leaf area index (LAI), and fractional vegetation cover (FVC)] from compact high-resolution imaging spectrometer (CHRIS) images and the atmospheric temperature, moisture, and ozone profiles from infrared atmospheric sounding interferometer (IASI) data.

Satellite-derived soil moisture (SM) products have become an important information source for the study of land surface processes in hydrology and land monitoring. Characterizing and estimating soil memory and persistence from satellite observations is of paramount relevance, and has deep implications in ecology, water management, and climate modeling. In this work, we address the problem of SM persistence estimation from microwave sensors using several autocorrelation metrics that, unlike traditional approaches, build on accurate estimates of the autocorrelation function from nonuniformly sampled time series. We show how the choice of the autocorrelation estimator can have a dramatic impact on the SM persistence metrics derived thereof, particularly given the nonuniform nature of satellite observations, yet this fact has been overlooked to a large extent in literature. We give empirical evidence of performance using ground-based SM measurements, as well as L-band Soil Moisture and Ocean Salinity (SMOS) and C-band [Advanced Microwave Scanning Radiometer-2 (AMSR2), Advanced Scatterometer (ASCAT)] remotely sensed SM data. Experiments along transects allow us to scrutinize the inter-method consistency and the spatial–temporal characteristics of autocorrelation estimators. This motivates the introduction of novel measures of spatial–temporal autocorrelation and allows us to retrieve improved persistence estimates. Results over the Iberian Peninsula indicate the SM persistence patterns captured by L- and C-band microwave sensors over semiarid regions exhibit spatially concurrent patterns of persistence and support their combination in long-term data records. We conclude that accounting for the nonuniform nature of the satellite time series using robust autocorrelation estimations allows providing improved measures and spatial descriptions of SM persistence.

Atmospheric radiative transfer models (RTMs) simulate the light propagation in the Earth's atmosphere. With the evolution of RTMs, their increase in complexity makes them impractical in routine processing such as atmospheric correction. To overcome their computational burden, standard practice is to interpolate a multidimensional lookup table (LUT) of prestored simulations. However, accurate interpolation relies on large LUTs, which still implies large computation times for their generation and interpolation. In recent years, emulation has been proposed as an alternative to LUT interpolation. Emulation approximates the RTM outputs by a statistical regression model trained with a low number of RTM runs. However, a concern is whether the emulator reaches sufficient accuracy for atmospheric correction. Therefore, we have performed a systematic assessment of key aspects that impact the precision of emulating MODTRAN: 1) regression algorithm; 2) training database size; 3) dimensionality reduction (DR) method and a number of components; and 4) spectral resolution. The Gaussian processes regression (GPR) was found the most accurate emulator. The principal component analysis remains a robust DR method and nearly 20 components reach sufficient precision. Based on a database of 1000 samples covering a broad range of atmospheric conditions, GPR emulators can reconstruct the simulated spectral data with relative errors below 1% for the 95th percentile. These emulators reduce the processing time from days to minutes, preserving sufficient accuracy for atmospheric correction and providing model uncertainties and derivatives. We provide a set of guidelines and tools to design and generate accurate emulators for satellite data processing applications.

The modeling of Earth observation (EO) data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the domain knowledge and physical rules governing the system. Such models, however, need the correct specification of all interactions between variables in the problem and the appropriate parameterization is a challenge in itself. On the other hand, machine learning approaches are flexible data-driven tools, able to approximate arbitrarily complex functions, but lack interpretability and struggle when data are scarce or in extrapolation regimes. In this article, we argue thathybrid learning schemesthat combine both approaches can address all these issues efficiently. We introduceGaussian process (GP) convolution modelsfor hybrid modeling in EO problems. We specifically propose the use of a class of GP convolution models calledlatent force models(LFMs) for EO time series modeling, analysis, and understanding. LFMs are hybrid models that incorporate physical knowledge encoded in differential equations into a multioutput GP model. LFMs can transfer information across time series, cope with missing observations, infer explicit latent functions forcing the system, and learn parameterizations which are very helpful for system analysis and interpretability. We illustrate the performance in two case studies. First, we consider time series of soil moisture (SM) from active Advanced Scatterometer (ASCAT) and passive [SM and ocean salinity (SMOS), advanced microwave scanning radiometer-2 (AMSR2)] microwave satellites. We show how assuming a first-order differential equation as governing equation, the model automatically estimates the e-folding time or decay rate related to SM persistence and discovers latent forces related to precipitation. In the second case study, we show how the model can fill in gaps of leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from moderate resolution imaging spectroradiometer (MODIS) optical time series by exploiting their relations across different spatial and temporal domains. The proposed hybrid methodology reconciles the two main approaches in remote-sensing parameter estimation by blending statistical learning and mechanistic modeling.

Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of physics, like mass or energy conservation, so consistency and confidence are compromised. In this paper we describe the main challenges ahead in the field, and introduce several ways to live in the Physics and machine learning interplay: encoding differential equations from data, constraining data-driven models with physics-priors and dependence constraints, improving parameterizations, emulating physical models, and blending data-driven and process-based models. This is a collective long-term AI agenda towards developing and applying algorithms capable of discovering knowledge in the Earth system.

Image classification has become one of the most common applications in remote sensing yielding to the creation of a variety of operational thematic maps at multiple spatio-temporal scales. The information contained in these maps summarizes key characteristics related with the physical environment and provides fundamental information of the Earth for vegetation monitoring or land use status over time. However, high spatial resolution land cover maps are usually only produced for specific small regions or in an image tile. We present a general methodology to obtain a high spatial resolution land cover maps using Landsat spectral information, the powerful Google Earth Engine platform, and operational coarse classification schemes such as the MODIS (MOD12) land cover. After the experimental analysis for different regions, we conclude that the method allows to successfully learn the MODIS Plant Functional Type classification scheme at 500 m pixel resolution which greatly improves the level of spatial detail when the machine learning model is applied to Landsat pixel resolution (30 m) reflectance data.

Agriculture monitoring, and in particular food security, requires near real-time information on crop growing conditions for early detection of possible production deficits. In this work, we propose the use of Gaussian processes (GPs). together with in-situ, EO and ERA-Interim climate reanalysis data for crop yield forecasting. Country-level agricultural survey data from FAOSTAT are used for quantitative assessment. The study is conducted in the framework of the ASAP (Anomaly hot Spots of Agricultural Production) early warning decision support system of the European Commission, which aims at providing timely information about possible crop production anomalies worldwide. After grouping countries with similar growing season periods, we We show that GP models allow predicting the yield of the different planted crops within such groups with coefficient of determination R2 ranging from 0.5 to 0.95. For each country and crop, a better fit that the mean of the data is obtained in all cases, and the errors obtained are comparable to the ones obtained for the group. The proposed modelling framework can be potentially adopted in ASAP operations to forecast national level crop yield and production.

High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.

In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kernel-based) method for DR that solves the previous problems without making any assumption on the statistics of the grouped data. The presented formulation considers distribution embeddings in reproducing kernel Hilbert spaces and performs standard least squares regression with the empirical means therein. A flexible version to deal with multisource data of different dimensionality and sample sizes is also presented and evaluated. It allows working with the native spatial resolution of each sensor, avoiding the need for matchup procedures. Noting the large computational cost of the approach, we introduce an efficient version via random Fourier features to cope with millions of points and groups. Real experiments involve the Soil Moisture Active Passive (SMAP) vegetation optical depth (VOD) data for the estimation of crop production in the U.S. Corn Belt and the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) reflectances for the estimation of aerosol optical depth (AOD). An exhaustive empirical evaluation of the method is done against naive (linear and nonlinear) approaches based on input-space means as well as previously presented methods for MIL. We provide source code of our methods in http://isp.uv.es/code/dr.html.

In this work we describe the methodology applied for the retrieval of global LAI, FAPAR and FVC from Advanced Very High Resolution Radiometer (AVHRR) on board the Meteorological-Operational (MetOp) polar orbiting satellites also known as EUMETSAT Polar System (EPS). A novel approach has been developed for the joint retrieval of three parameters (LAI, FVC, and FAPAR) instead of training one model per parameter. The method relies on multi-output Gaussian Processes Regression (GPR) trained over PROSAIL EPS simulations. A sensitivity analysis is performed to assess several sources of uncertainties in retrievals and maximize the positive impact of modeling the noise in training simulations. We describes the main features of the operational processing chain along with the current status of the global EPS vegetation products, including details about its overall quality and preliminary assessment of the products based on intercomparsion with equivalent (MODIS, PROBA-V) satellite vegetation products.

In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer.

Measurements of soil moisture are needed for a better global understanding of the land surface-climate feedbacks at both the local and the global scale. Satellite sensors operating in the low frequency microwave spectrum (from 1 to 10 GHz) have proven to be suitable for soil moisture retrievals. These sensors now cover nearly 4 decades thus allowing for global multi-mission climate data records. In this paper, we assess the possibility of using L-band (SMOS) and C-band (AMSR2, ASCAT) remotely sensed soil moisture time series for the global estimation of soil moisture persistence. A multi -output Gaussian process regression model is applied to ensure spatio-temporal coverage of the satellite data sets. It allows a robust computation of temporal autocorrelation and e- folding times. Results over a selection of catchments reveals general agreement between the response of in-situ and satellite microwave observations to hydrological processes. The response of the uppermost-modeled soil moisture layer of GLDAS-1-Noah agrees well with that of the observations, whereas major differences are displayed by MERRA2 reanalysis. The temporal dynamics of the three microwave sensors are shown to be consistent, close to in-situ and to GLDAS-1- Noah, which supports their combination for the global estimation soil moisture persistence.

End-to-end mission performance simulators (E2ES) are suitable tools to accelerate satellite mission development from concet to deployment. One core element of these E2ES is the generation of synthetic scenes that are observed by the various instruments of an Earth Observation mission. The generation of these scenes rely on Radiative Transfer Models (RTM) for the simulation of light interaction with the Earth surface and atmosphere. However, the execution of advanced RTMs is impractical due to their large computation burden. Classical interpolation and statistical emulation methods of pre-computed Look-Up Tables (LUT) are therefore common practice to generate synthetic scenes in a reasonable time. This work evaluates the accuracy and computation cost of interpolation and emulation methods to sample the input LUT variable space. The results on MONDTRAN-based top-of-atmosphere radiance data show that Gaussian Process emulators produced more accurate output spectra than linear interpolation at a fraction of its time. It is concluded that emulation can function as a fast and more accurate alternative to interpolation for LUT parameter space sampling.

This letter introduces warped Gaussian process (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such a prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll content from multispectral data, vegetation parameters (chlorophyll, leaf area index, and fractional vegetation cover) from hyperspectral data, and in the detection of the causal direction in a collection of 28 bivariate geoscience and remote sensing causal problems. The model consistently performs better than the standard GP and the more advanced heteroscedastic GP model, both in terms of accuracy and more sensible confidence intervals.

This paper presents the development and implementation of a cloud detection algorithm for Proba-V. Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant sources of error in both sea and land cover biophysical parameter retrieval. The objective of the algorithms presented in this paper is to detect clouds accurately providing a cloud flag per pixel. For this purpose, the method exploits the information of Proba-V using statistical machine learning techniques to identify the clouds present in Proba-V products. The effectiveness of the proposed method is successfully illustrated using a large number of real Proba-V images.

Emissivity is one of the most important parameters to improve the determination of the troposphere properties (thermodynamic properties, aerosols and trace gases concentration) and it is essential to estimate the radiative budget. With the second generation of infrared sounders, we can estimate emissivity spectra at high spectral resolution, which gives us a global view and long-term monitoring of continental surfaces. Statistically, this is an ill-posed retrieval problem, with as many output variables as inputs. We here propose nonlinear multi-output statistical regression based on kernel methods to estimate spectral emissivity given the radiances. Kernel methods can cope with high-dimensional input-output spaces efficiently. We give empirical evidence of models performance on Infrared Atmospheric Sounding Interferometer (IASI) simulated data. Kernel regression model largely improves previous least squares linear regression model quantitatively, with an average reduction of 25% in mean-square error.

The vast amount of data acquired by current high resolution Earth observation satellites implies some technical challenges to be faced. Google Earth Engine (GEE) platform provides a framework for the development of algorithms and products built over this data in an easy and scalable manner. In this paper, we take advantage of the GEE platform capabilities to exploit the wealth of information in the temporal dimension by processing a long time series of satellite images. A cloud detection algorithm for Landsat-8, which uses previous images of the same location to detect clouds, is implemented and tested on the GEE platform.

Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimation with a manageable training data set, and their implementation into a Matlab-based MLRA toolbox for semiautomatic use. The AL methods were analyzed on their efficiency of improving the estimation accuracy of the leaf area index and chlorophyll content based on PROSAIL simulations. Each of the implemented methods outperformed random sampling, improving retrieval accuracy with lower sampling rates. Practically, AL methods open opportunities to feed advanced MLRAs with RTM-generated training data for the development of operational retrieval models.

This paper deals with the development and implementation of a cloud screening algorithm for image time series, with the focus on the forthcoming Sentinel-2 satellites to be launched under the ESA Copernicus Programme. The proposed methodology is based on kernel ridge regression and exploits the temporal information to detect anomalous changes that correspond to cloud covers. The huge data volumes to be processed when dealing with high temporal, spatial, and spectral resolution datasets motivate the implementation of the algorithm within distributed computer resources. In consequence, an operational cloud screening service has been specifically designed and implemented in the frame of the Sentinels Synergy Framework (SenSyF). The effectiveness of the proposed method is successfully illustrated using a time series dataset with a 5-day revisit derived from SPOT-4 at high resolution, which has been collected by ESA in preparation for the exploitation of the Sentinel-2 mission.

This paper focuses on biophysical parameter retrieval based on Gaussian Processes (GPs). Very often an arbitrary transformation is applied to the observed variable (e.g. chlorophyll content) to better pose the problem. This standard practice essentially tries to linearize/uniformize the distribution by applying non-linear link functions like the logarithmic, the exponential or the logistic functions. In this paper, we propose to use a GP model that automatically learns the optimal transformation directly from the data. The so-called warped GP regression (WGPR) presented in [1] models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll content, which outperforms the regular GPR and a more advanced heteroscedastic GPR model.

Solar irradiation prediction is an important problem in geosciences with direct applications in renewable energy. Recently, a high number of machine learning techniques have been introduced to tackle this problem, mostly based on neural networks and support vector machines. Gaussian process regression (GPR) is an alternative nonparametric method that provided excellent results in other biogeophysical parameter estimation. In this letter, we evaluate GPR for the estimation of solar irradiation. Noting the nonstationary temporal behavior of the signal, we develop a particular time-based composite covariance to account for the relevant seasonal signal variations. We use a unique meteorological data set acquired at a radiometric station that includes both measurements and radiosondes, as well as numerical weather prediction models. We show that the so-called temporal GPR outperforms ten state-of-the-art statistical regression algorithms (even when including time information) in terms of accuracy and bias, and it is more robust to the number of predictions used.

This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the γ-filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and electroencephalographic time series prediction, complex nonlinear system identification, and adaptive antenna array processing demonstrate the potential of the approach for scenarios where recursivity and nonlinearity have to be readily combined.

This paper presents a multitemporal feature extraction method based on kernels that is particularly designed for change detection. The method provides features that maximize specific changes between two dates while minimizing sources of errors, such as residual land-cover changes and misregistration errors, in the time series. The extracted features computed in the kernel feature space can deal with non-linear relations between samples at different dates. Moreover, no supervised information is required to find the changes of interest for the selected dates in the time series. The effectiveness of the proposed method is successfully illustrated in a cloud masking application using a Landsat time series. Results show that the proposed method provides the most discriminative features in terms of cloud detection when confronted with state of the art linear and nonlinear unsupervised feature extraction algorithms. In particular, extracted features with the proposed method enable automatic cloud detection in multispectral time series.

This paper presents a synergistic cloud detection algorithm that has been developed for processing simultaneous observations from AATSR and MERIS sensors on-board ENVISAT. The main objective of this work is to explore sensor synergies in order to increase the cloud detection accuracy and provide a reliable cloud mask. This is of paramount importance in the framework of the ESA climate change initiative for clouds (Cloud CCI), which aims to provide long time series of cloud properties at a global scale from satellite data. The cloud detection algorithm is based on an ensemble of artificial neural networks, where the outputs of different dedicated models are combined to provide more accurate and robust predictions. The performance of the method has been tested on a large number of real images, and provides higher classification accuracy than other methods, especially when spatial information from the images is included in the classifiers.

In this paper, we study the applicability of active learning (AL) in operative scenarios. More particularly, we consider the well-known contradiction between the AL heuristics, which rank the pixels according to their uncertainty, and the user's confidence in labeling, which is related to both the homogeneity of the pixel context and user's knowledge of the scene. We propose a filtering scheme based on a classifier that learns the confidence of the user in labeling, thus minimizing the queries where the user would not be able to provide a class for the pixel. The capacity of a model to learn the user's confidence is studied in detail, also showing that the effect of resolution in such a learning task. Experiments on two QuickBird images of different resolutions (with and without pansharpening) and considering committees of users prove the efficiency of the filtering scheme proposed, which maximizes the number of useful queries with respect to traditional AL.

We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering a source and a destination domain, the adaptation is carried out by transforming one data set to the other using an appropriate nonlinear deformation. The eventually nonlinear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the samples in one domain are projected onto the other, thus allowing the application of any classifier or regressor in the transformed domain. Experiments on challenging remote sensing scenarios, such as multitemporal very high resolution image classification and angular effects compensation, show the validity of the proposed method to match-related domains and enhance the application of cross-domains image processing techniques.

When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. Moreover, the efficiency of the search is also an issue, since the samples labeled as background are not used by target detectors such as the support vector data description (SVDD). In this work we propose a competitive and effective approach to identify the most relevant training samples for one-class classification based on the use of an active learning strategy. The SVDD classifier is first trained with insufficient target examples. It is then used to detect the most informative samples to be labeled by a user through active learning techniques. By selecting unlabeled samples in a smart way and by adopting a diversity criterion, it is possible to obtain an accurate description of the class of interest with a relatively small number of training samples. The performance of the proposed method is illustrated in a change detection scenario and is validated by comparison with state-of-art active learning techniques originally developed for multiclass problems.

In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according to their uncertainty, and the confidence of the user in labeling, which is related both to the homogeneity of the pixel context and to the knowledge of the user of the scene. In this paper, we propose a two-steps procedure based on a filtering scheme to learn the confidence of the user in labeling. This way, candidate training pixels are ranked according both to their uncertainty and to the chances of being labeled correctly by the user. In this way, we avoid the queries where the user would not be able to provide a class for the pixel. We consider the capacity of a model in learning the user's confidence and report experiments on a QuickBird image: the filtering scheme proposed maximizes the number of useful queries with respect to traditional active learning.

This paper evaluates nonlinear retrieval methods to derive atmospheric properties from hyperspectral infrared sounding spectra, with emphasis on the retrieval of temperature, humidity, and ozone atmospheric profiles. We concentrate on the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-A satellite data for the future Meteosat Third Generation Infrared Sounder (MTG-IRS). The methods proposed in this work are compared in terms of both accuracy and speed with the current MTG-IRS L2 processing concept, which processes MetOp-IASI and proxy MTG-IRS data. The official chain consists of a principal component extraction, typically referred to as empirical orthogonal functions (EOF) and a subsequent canonical linear regression. This research proposes the evaluation of some other methodological advances considering: 1) other linear feature extraction methods instead of EOF, such as partial least squares; and 2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear regression models considered in this work are artificial neural networks and kernel ridge regression as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models yield better results than linear retrieval for both MetOp-IASI and MTG-IRS synthetic and real data. Averaged gains throughout the column of +1.8 K and +2.2 K are obtained for temperature profile estimation from MetOp-IASI and IRS data, respectively. Similar gains are obtained for the estimation of dew point temperatures. In both variables, these improvements are more noticeable in lower atmospheric layers. The combination of models makes the retrieval more robust, improves the accuracy, and decreases the estimated bias. The nonlinear statistical approach is successfully compared to optimal estimation (OE) in terms of accuracy, bias and computational cost. These results confirm the potential of statistical nonlinear inversion techniques for the retrieval of atmospheric profiles.

We propose a semiautomatic procedure to generate land cover maps from remote sensing images. The proposed algorithm starts by building a hierarchical clustering tree, and exploits the most coherent pixels with respect to the available class information. For a given amount of labeled pixels, the algorithm returns both classification and confidence maps. Since the quality of the map depends of the number and informativeness of the labeled pixels, active learning methods are used to select the most informative samples to increase confidence in class membership. Experiments on four different data sets, accounting for hyperspectral and multispectral images at different spatial resolutions, confirm the effectiveness of the proposed approach, and how active learning techniques reduce the uncertainty of the classification maps. Specifically, more accurate results with fewer labeled samples are obtained. Inclusion of spatial information in the classifiers drastically improves the classification accuracy, leading to faster convergence curves and tighter confidence intervals. In conclusion, the presented algorithm provides efficient image classification and, at the same time, yields a confidence map that may be very useful in many Earth observation applications.

This paper proposes the use of kernel ridge regression (KRR) to derive surface and atmospheric properties from hyperspectral infrared sounding spectra. We focus on the retrieval of temperature and humidity atmospheric profiles from Infrared Atmospheric Sounding Interferometer (MetOp-IASI) data, and provide confidence maps on the predictions. In addition, we propose a scheme for the identification of anomalies by supervised classification of discrepancies with the ECMWF estimates. For the retrieval, we observed that KRR clearly outperformed linear regression. Looking at the confidence maps, we observed that big discrepancies are mainly due to the presence of clouds and low emissivities in desert areas. For the identification of anomalies, we observed that the confidence intervals provided by the KRR may help in discarding big errors. High detection accuracy (around 90%) is achieved by a support vector machine, which largely outperforms standard linear and nonlinear classifiers.

A semiautomatic procedure to generate classification maps of remote sensing images is proposed. Starting from a hierarchical unsupervised classification, the algorithm exploits the few available labeled pixels to assign each cluster to the most probable class. For a given amount of labeled pixels, the algorithm returns a classified segmentation map, along with confidence levels of class membership for each pixel. Active learning methods are used to select the most informative samples to increase confidence in the class membership. Experiments on a AVIRIS hyperspectral image confirm the effectiveness of the method, especially when used with active learning query functions and spatial regularization.

In this work we present an adaptation algorithm focused on the description of the measurement changes under different acquisition conditions. The adaptation is carried out by transforming the manifold in the first observation conditions into the corresponding manifold in the second. The eventually non-linear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the labeled samples in the first are projected into the second domain, thus allowing the application of any classifier in the transformed domain. Experiments on VHR series of images show the validity of the proposed method to adapt the classifiers to related domains.

Hyperspectral data lossy compression has not yet achieved global acceptance in the remote sensing community, mainly because it is generally perceived that using compressed images may affect the results of posterior processing stages. This possible negative effect, however, has not been accurately characterized so far. In this letter, we quantify the impact of lossy compression on two standard approaches for hyperspectral data exploitation: spectral unmixing, and supervised classification using support vector machines. Our experimental assessment reveals that different stages of the linear spectral unmixing chain exhibit different sensitivities to lossy data compression. We have also observed that, for certain compression techniques, a higher compression ratio may lead to more accurate classification results. Even though these results may seem counterintuitive, this work explains these observations in light of the spatial regularization and/or whitening that most compression techniques perform and further provides recommendations on best practices when applying lossy compression prior to hyperspectral data classification and/or unmixing.

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

This paper presents two semisupervised one-class support vector machine (OC-SVM) classifiers for remote sensing applications. Inone-classimage classification, one tries to detect pixels belonging to one of the classes in the image and reject the others. When few labeled pixels of only one class are available, obtaining a reliable classifier is a difficult task. In the particular case of SVM-based classifiers, this task is even harder because the free parameters of the model need to be finely adjusted, but no clear criterion can be adopted. In order to improve the OC-SVM classifier accuracy and alleviate the problem of free-parameter selection, the information provided by unlabeled samples present in the scene can be used. In this paper, we present two state-of-the-art algorithms for semisupervised one-class classification for remote sensing classification problems. The first proposed algorithm is based on modifying the OC-SVM kernel by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples. The second one is based on a simple modification of the standard SVM cost function which penalizes more the errors made when classifying samples of the target class. The good performance of the proposed methods is illustrated in four challenging remote sensing image classification scenarios where the goal is to detect one of the classes present on the scene. In particular, we present results for multisource urban monitoring, hyperspectral crop detection, multispectral cloud screening, and change-detection problems. Experimental results show the suitability of the proposed techniques, particularly in cases with few or poorly representative labeled samples.

We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results in the estimation of oceanic chlorophyll concentration and leaf membrane state confirm GPs as adequate, alternative non-parametric methods for biophysical parameter estimation. GPs are also analyzed by scrutinizing the predictive variance, the estimated noise variance, and the relevance of each feature after optimization.

This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more accurate and robust ensemble of classifiers. The proposed classifier is tested on more than 80 coregistered MERIS/AATSR images providing better classification accuracy than the official cloud flags and available operational cloud screening algorithms for MERIS and AATSR. Moreover, thanks to the synergy of both sensors, it correctly classifies critical cloud-screening problems such as snow and ice covers over land and sun-glint over ocean.

This letter presents two kernel-based methods for semisupervised regression. The methods rely on building a graph or hypergraph Laplacian with both the available labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). Given the high computational burden involved, we present two alternative formulations based on the Nystrom method and the incomplete Cholesky factorization to achieve operational processing times. The semisupervised SVR algorithms are successfully tested in multiplatform leaf area index estimation and oceanic chlorophyll concentration prediction. Experiments are carried out with both multispectral and hyperspectral data, demonstrating good generalization capabilities when a low number of labeled samples are available, which is usually the case in biophysical parameter retrieval.

Two kernel-based methods for semi-supervised regression are presented. The methods rely on building a graph or hypergraph Laplacian with both the labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). The semi-supervised SVR methods are sucessfully tested in LAI estimation and ocean chlorophyll concentration prediction from remotely sensed images.

This letter presents a semisupervised method based on kernel machines and graph theory for remote sensing image classification. The support vector machine (SVM) is regularized with the unnormalized graph Laplacian, thus leading to the Laplacian SVM (LapSVM). The method is tested in the challenging problems of urban monitoring and cloud screening, in which an adequate exploitation of the wealth of unlabeled samples is critical. Results obtained using different sensors, and with low number of training samples, demonstrate the potential of the proposed LapSVM for remote sensing image classification.

The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detection methods by formulating them in an adequate high-dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multisensor images with different levels of nonlinear sophistication. The binary support vector (SV) classifier and the one-class SV domain description classifier are evaluated by using both linear and nonlinear kernel functions. Good performance on synthetic and real multitemporal classification scenarios illustrates the generalization of the framework and the capabilities of the proposed algorithms.

This paper introduces the use of Relevance Vector Machines (RVM) for remote sensing hyperspectral image classification. We also include the Mahalanobis kernel in the formulation of the RVM to take into account the covariance of the features in the classification process. Experimental results in different scenarios confirm the accuracy and robustness of the proposed method, and also the ease of free parameters tuning.

This work evaluates a new semi-supervised classification framework based on kernel methods and graph theory. In particular, the support vector machine (SVM) is further regularized with the un-normalized graph Laplacian, thus leading to the proposed Laplacian SVM. The method is tested in the challenging problem of cloud screening where the objective is to identify clouds in multispectral images acquired by space-borne sensors working in the visible and near-infrared spectral range. Preliminary results obtained using MERIS/ENVISAT data show the potential of the proposed Laplacian SVM in several scenarios.

This paper presents simple but powerful combination methods of dedicated one-class classifiers (OCCs) for efficient remote sensing image classification. The mean and product combination rules are applied to the probabilistic outputs generated by OCCs, and the performance is illustrated in a urban monitoring application in which multi-sensor (optical and SAR) data and multi-source (spectral and contextual) features are available. Two OCCs are used as core parts: the classical mixture of Gaussians (MoG) and the support vector domain description (SVDD) classifier. The obtained results by combining SVDD classifier outputs show a clear improvement in the accuracy, and more robustness to high dimensional samples compared to both MoG and stacked approaches.

Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection

This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. The SVDD technique is compared with other standard single-class methods both in problems focused on the recognition of a single specific land-cover class and in multiclass problems. For the latter, we properly define an easily scalable multiclass architecture capable to deal with incomplete training data. Experimental results, obtained on different kinds of data (synthetic, hyperspectral, and multisensor images), point out the effectiveness of the SVDD technique and provide important indications for driving the choice of the classification technique and architecture in the presence of incomplete training data.

This letter presents a framework of composite kernel machines for enhanced classification of hyperspectral images. This novel method exploits the properties of Mercer's kernels to construct a family of composite kernels that easily combine spatial and spectral information. This framework of composite kernels demonstrates: 1) enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only: 2) flexibility to balance between the spatial and spectral information in the classifier; and 3) computational efficiency. In addition, the proposed family of kernel classifiers opens a wide field for future developments in which spatial and spectral information can be easily integrated.

Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA2K) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer's kernels. This general class can improve model flexibility by emphasizing the input-output cross information (SVM-ARMA4K), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA2K and SVR-ARMA4K). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems.