We calculate water-mass (WM) transformation and formation rates in thermohaline (θ-S), density (σ) and geographic coordinates over three years for three ocean basins; the North Atlantic, North Pacific and Southern Ocean by partitioning surface heat and freshwater fluxes into bins of sea surface salinity and temperature (SSS, SST) and density (σ). The three years correspond to the overlap between the SMOS and Aquarius SSS products with the SST product being that from OSTIA. Surface heat and freshwater fluxes were taken from the NOCS climatology V2.0, OAFLUX and the satellite based CMORPH dataset for evaporation and precipitation respectively. Results from SMOS and Aquarius satellite derived datasets are inter-compared followed by a comparison between the literature locations of Mode Waters (MW) in σ, θ-S and geographic co-ordinates and SMOS SSS. Then a sensitivity experiment was performed – utilising a MonteCarlo (MC) simulation – where we show the relative contributions of SSS and SST on WM formation through perturbations introduced to the satellite SSS and SST datasets. We aim to demonstrate and evaluate the feasibility of satellites at characterising the distribution and dynamics of WM’s via a comparison with literature.
In some key operational domains, users are not specially interested in obtaining an exhaustive map with all the thematic classes present in an area of interest, but rather in identifying accurately a single class of interest. In this paper, we present a novel partially supervised classification technique that faces this interesting practical and methodological problem. We have adopted a two-stage classification scheme based on an unsupervised approach, which allows us to introduce supervised information about the class of interest without an additional sample labeling. The first stage of the process consists in an initial clustering of the image using the Self-Organizing Map algorithm. The second stage consists in a partially supervised hierarchical joint of clusters. We modify the employed criterion of similarity by introducing fuzzy membership functions that make use of the supervised information. The method is tested on urban monitoring, where the objective is to produce an automatic classification of 'Urban/Non-Urban' by using optical and radar data (Landsat TM and 35-days interferometric pairs of ERS2 SAR). We compare classification accuracy of the proposed method to its parametric version, which uses the Expectation-Maximization algorithm. The good performance confirms the validity of the proposed approach: 90% classification accuracy using supervised information only in the coherence map.
A novel data fusion approach to partially supervised classification problems is presented, which allows a specific land-cover class of interest to be mapped by using only training samples belonging to such class. This represents a significant operational advantage in many application domains where end-users require information products for the monitoring of a specific or few land cover classes (e.g., forestry, urban monitoring) of interest. The proposed technique overcomes one of the main methodological drawbacks of this type of problems: i.e., the lack of prior knowledge on the statistics of the unknown classes present in the scene under consideration. Experiments carried out on a multisource data set demonstrate the validity of the proposed technique.
A novel automatic approach to the unsupervised detection of changes in a pair of remote-sensing images acquired on the same geographical area at different times is presented. The proposed approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, we propose an iterative non-parametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in the difference image generated by the comparison of the two images. Such a technique exploits the effectiveness of two theoretically well-founded estimation procedures: the reducedparzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, on the basis of the resulting non-parametric estimates, a markov random field (MRF) approach is used for modeling the spatial-contextual information contained in the multitemporal images considered. The non-parametric nature of the proposed method allows its application to different kind of remote-sensing images (e.g., SAR and optical images). Experimental results, obtained on a set of multitemporal remotesensing images, confirm the effectiveness of the proposed technique.
Several applications of supervised classification of remote- sensing images involve the periodical mapping of a fixed set of land-cover classes on a specific geographical area. These applications require the availability of a training set (and hence of ground-truth information) for each new image analyzed. However, the collection of ground truth information is a complex and expensive process that only in few cases can be performed every time that a new image is acquired. This represents a serious drawback of classical supervised classifiers. In order to overcome such a drawback, an unsupervised retraining technique for supervised maximum- likelihood (ML) classifiers is proposed in this paper. Such a technique, which is based on the Expectation-Maximization (EM) algorithm, allows the statistical parameters of an already trained ML classifier to be updated so that a new image, for which a training set is not available, can be classified with an acceptable accuracy. Experiments, which have been carried out on a multitemporal data set, confirm the effectiveness of the proposed technique.
A supervised technique for training Radial Basis Function (RBF) neural classifiers is proposed. Such a technique, unlike traditional ones, considers the class-memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The proposed method has significant advantages over traditional ones in terms of classification accuracy and stability of the network. Experimental results, carried out on a multisensor remote-sensing data set, confirm the validity of the proposed technique.
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