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Landmark recognition and matching is a critical step in many image navigation and registration models for geostationary satellite services, as well as to maintain the geometric quality assessment in the instrument data processing chain of earth observation satellites. Matching the landmark accurately is of paramount relevance, and the process can be strongly impacted by the cloud contamination of a given landmark. This paper introduces a complete pattern recognition methodology able to detect the presence of clouds over landmarks using Meteosat second generation (MSG) data. The methodology is based on the ensemble combination of dedicated support vector machines dependent on the particular landmark and illumination conditions. This divide-and-conquer strategy is motivated by the data complexity and follows a physically based strategy that considers variability both in seasonality and illumination conditions along the day to split observations. In addition, it allows training the classification scheme with millions of samples at an affordable computational costs. The image archive was composed of 200 landmark test sites with near 7 million multispectral images that correspond to MSG acquisitions during 2010. Results are analyzed in terms of cloud detection accuracy and computational cost. We provide illustrative source code and a portion of the huge training data to the community. [ABSTRACT FROM AUTHOR]

Earth observation through remote sensing images allows the accurate characterization and identification of materials on the surface from space and airborne platforms. Multiple and heterogeneous image sources can be available for the same geographical region: multispectral, hyperspectral, radar, multitemporal, and multiangular images can today be acquired over a given scene. These sources can be combined/fused to improve classification of the materials on the surface. Even if this type of systems is generally accurate, the field is about to face new challenges: the upcoming constellations of satellite sensors will acquire large amounts of images of different spatial, spectral, angular, and temporal resolutions. In this scenario, multimodal image fusion stands out as the appropriate framework to address these problems. In this paper, we provide a taxonomical view of the field and review the current methodologies for multimodal classification of remote sensing images. We also highlight the most recent advances, which exploit synergies with machine learning and signal processing: sparse methods, kernel-based fusion, Markov modeling, and manifold alignment. Then, we illustrate the different approaches in seven challenging remote sensing applications: 1) multiresolution fusion for multispectral image classification; 2) image downscaling as a form of multitemporal image fusion and multidimensional interpolation among sensors of different spatial, spectral, and temporal resolutions; 3) multiangular image classification; 4) multisensor image fusion exploiting physically-based feature extractions; 5) multitemporal image classification of land covers in incomplete, inconsistent, and vague image sources; 6) spatiospectral multisensor fusion of optical and radar images for change detection; and 7) cross-sensor adaptation of classifiers. The adoption of these techniques in operational settings will help to monitor our planet from space in the very near future. [ABSTRACT FROM PUBLISHER]

This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. The construction of the kernel is very simple and intuitive: Two samples should belong to the same class if they consistently belong to the same clusters at different scales. The effectiveness of the proposed method is successfully illustrated in multi- and hyperspectral remote sensing image classification and biophysical parameter estimation problems. Accuracy improvements in the range between +5% and 15% over standard principal component analysis (PCA), +4% and 15% over kernel PCA, and +3% and 10% over KPLS are obtained on several images. The average gain in the root-mean-square error of +5% and reductions in bias estimates of +3% are obtained for biophysical parameter retrieval compared to standard PCA feature extraction. [ABSTRACT FROM PUBLISHER]

This letter introduces a simple method for including invariances in support-vector-machine (SVM) remote sensing image classification. We design explicit invariant SVMs to deal with the particular characteristics of remote sensing images. The problem of including data invariances can be viewed as a problem of encoding prior knowledge, which translates into incorporating informative support vectors (SVs) that better describe the classification problem. The proposed method essentially generates new (synthetic) SVs from the obtained by training a standard SVM with the available labeled samples. Then, original and transformed SVs are used for training the virtual SVM introduced in this letter. We first incorporate invariances to rotations and reflections of image patches for improving contextual classification. Then, we include an invariance to object scale in patch-based classification. Finally, we focus on the challenging problem of including illumination invariances to deal with shadows in the images. Very good results are obtained when few labeled samples are available for classification. The obtained classifiers reveal enhanced sparsity and robustness. Interestingly, the methodology can be applied to any maximum-margin method, thus constituting a new research opportunity. [ABSTRACT FROM PUBLISHER]

Data from current medium-spatial-resolution imaging spectroradiometers are used for land-cover mapping and land-cover change detection at regional to global scales. However, few landscapes are homogeneous at these scales, and this creates the so-called mixed-pixel problem. In this context, this study explores the use of the linear spectral mixture model to extract subpixel land-cover composition from medium-spatial-resolution data. In particular, a time series of MEdium Resolution Imaging Spectrometer (MERIS) full-resolution (FR; pixel size of 300 m) images acquired over The Netherlands is used to illustrate this study. The Netherlands was selected because of the following: 1) the fragmentation of its landscapes and 2) the availability of a high-spatial-resolution land-cover data set (LGN5) which can be used as a reference. The question then is to what extent a multitemporal unmixing of MERIS FR data delivers land-cover information comparable with the one provided by the LGN5. To this end, fully constrained linear spectral unmixing is applied to each individual MERIS image and to the multitemporal composite. The unmixing results are validated at both subpixel and per-pixel scales and at two thematic aggregation levels (12 and 4 land-cover classes). The obtained results indicate that the described unmixing approach yields moderate results for the 12-class case and good results for the 4-class case. These results might be explained by MERIS preprocessing steps, gridding effects, vegetation phenophases, and spectral class separability. [ABSTRACT FROM AUTHOR]

Earth observation satellites currently provide a large volume of images at different scales. Most of these satellites provide global coverage with a revisit time that usually depends on the instrument characteristics and performance. Typically, medium-spatial-resolution instruments provide better spectral and temporal resolutions than mapping-oriented high-spatial-resolution multispectral sensors. However, in order to monitor a given area of interest, users demand images with the best resolution available, which cannot be reached using a single sensor. In this context, image fusion may be effective to merge information from different data sources. In this letter, an image fusion approach based on multiresolution and multisource spatial unmixing is used to obtain a composite image with the spectral and temporal characteristics of medium-spatial-resolution instrument along with the spatial resolution of high-spatial-resolution image. A time series of Landsat/TM and ENVISAT/MERIS Full Resolution images acquired in the 2004 European Space Agency (ESA) Spectra Barrax Campaign illustrates the method's capabilities. The qualitative and quantitative assessments of the product images are given. The proposed methodology is general enough to be applied to similar sensors, such as the multispectral instruments which will fly on board the ESA GMES Sentinel-2 and Sentinel-3 upcoming satellite series. [ABSTRACT FROM PUBLISHER]

Earth observation satellites provide a valuable source of data which when conveniently processed can be used to better understand the Earth system dynamics. In this regard, one of the prerequisites for the analysis of satellite image time series is that the images are spatially coregistered so that the resulting multitemporal pixel entities offer a true temporal view of the area under study. This implies that all the observations must be mapped to a common system of grid cells. This process is known as gridding and, in practice, two common grids can be used as a reference: 1) a grid defined by some kind of external data set (e.g., an existing land-cover map) or 2) a grid defined by one of the images of the time series. The aim of this paper is to study the impact that gridding has on the quality of satellite time series. More precisely, the impact of the so-called gridding artifacts is quantified using a time series of 12 images acquired over The Netherlands by the Medium Resolution Imaging Spectrometer (MERIS). First, the impact of selecting a reference grid is evaluated in terms of geolocation errors and pixel overlap. Then, the effect of observation geometry is studied as nongeostationary satellites, like MERIS, can acquire images from the same area from a number of orbits. Finally, a high-resolution land-cover data set is used to account for temporal information consistency (pixel homogeneity in terms of land-cover composition). Results have shown an average pixel overlap with the nearest pixel between 20% and 41% depending on the selected reference grid and on the differences in observation geometry. These results indicate that inappropriate gridding might result in collocated time series that are not adequate for temporal studies at pixel level (particularly over nonhomogeneous areas) and that, in any case, it is interesting to identify areas with low pixel overlap in order to further analyze the reliability of the products derived over these areas. [ABSTRACT FROM AUTHOR]

In this paper, we present a configurable multispectral imaging system based on an acousto-optic tunable filter (AOTF). Typically, AOTFs are used to filter a single wavelength at a time, but thanks to the use of a versatile sweeping frequency generator implemented with a direct digital synthesizer, the imager may capture a configurable spectral range. Experimental results show a good spectral and imaging response of the system for spectral bandwidth up to a 50 nm. [ABSTRACT FROM AUTHOR]