This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification
method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture
radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered
input images. For each class and each input channel, the class-conditional marginal probability density functions
are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution
is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and
Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the
number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step,
we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree
structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution
data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.
This paper addresses the problem of the classification of very high resolution (VHR) SAR amplitude images of
urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional
probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such
as those extracted by the greylevel co-occurrency method, are also integrated in the technique, as they allow
to improve the discrimination of urban areas. Copulas are applied to estimate bivariate joint class-conditional
statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint
distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics
scheme for energy minimization. Experimental results with COSMO-SkyMed and TerraSAR-X images point out
the accuracy of the proposed method, also as compared with previous contextual classifiers.
In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic
aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian
image classification and a finite mixture technique for probability density function estimation. The finite mixture
modeling is done via a recently proposed dictionary-based stochastic expectation maximization approach for
SAR amplitude probability density function estimation. For modeling the joint distribution from marginals
corresponding to single polarimetric channels we employ copulas. The accuracy of the developed semiautomatic
supervised algorithm is validated in the application of wet soil classification on several high resolution SAR
images acquired by TerraSAR-X and COSMO-SkyMed.
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate
models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for
the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is
an extension of previously existing method for lower resolution images. The method integrates the stochastic
expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique
to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of
parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR-specific
pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root,
Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and
the algorithm to automatically estimate the optimal number of mixture components. The experimental results
with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed
algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of
quantitive accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective
on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes.
Change-detection methods represent powerful tools for monitoring the evolution of the state of the Earth's surface.
In order to optimize the accuracy of the change maps, a multiscale approach can be adopted, in which
observations at coarser and finer scales are jointly exploited. In this paper, a multiscale contextual unsupervised
change-detection method is proposed for optical images, which is based on discrete wavelet transforms and
Markov random fields. Wavelets are applied to the difference image to extract multiscale features and Markovian
data fusion is used to integrate both these features and the spatial contextual information in the change-detection
process. Expectation-maximization and Besag's algorithms are used to estimate the model parameters. Experiments
on real optical images points out the improved effectiveness of the method, as compared with single-scale
approaches.
This paper investigates an ensemble framework which is proposed for accurate classification of hyperspectral
data. The usefulness of the method, designed to be a simple and robust supervised classification tool, is assessed
on real data, characterized by classes with very similar spectral responses, and limited amount of ground truth
labeled training samples. The method is inspired by the framework of the Random Forests method proposed
by Breiman (2001). The success of the method relies on the use of support vector machines (SVMs) as base
classifiers, the freedom of random selection of input features to create diversity in the ensemble, and the use of
the weighted majority voting scheme to combine classification results. Although not fully optimized, a simple
and feasible solution is adopted for tuning the SVM parameters of the base classifiers, aiming its use in practical
applications. Moreover, the effect of an additional pre-processing module for an initial feature reduction is
investigated. Encouraging results suggest the proposed method as promising, in addition to being easy to
implement.
Land surface temperature (LST) and sea surface temperature (SST) are important quantities for many environmental
models, and remote sensing is a feasible and promising way to estimate them on a regional and global
scale. In order to estimate LST and SST from satellite data many algorithms have been devised, most of which
require a-priori information about the surface and the atmosphere. However, the high variability of surface and
atmospheric parameters causes these traditional methods to produce significant estimation errors, thus making
their application on a global scale critical. A recently proposed approach involves the use of support vector
machines (SVMs). Based on satellite data and corresponding in-situ measurements, they generate an approximation
of the relation between them, which can be used subsequently to estimate unknown surface temperatures
for additional satellite data. Such a strategy requires the user to set several internal parameters.
In this paper a method is proposed for automatically setting these parameters to values that lead to minimum
estimation errors. This is achieved by minimizing a functional correlated to regression errors (i.e., the "spanbound"
upper bound on the leave-one-out error) which can be computed using only the training set, without the
need for a further validation set. In order to minimize this functional, the Powell's algorithm is used, because
it is applicable also to nondifferentiable functions. Experimental results generated by the proposed method turn
out to be very similar to those obtained by cross-validation and by a grid search for the parameter configuration
yielding the best test-set accuracy, although with a dramatic reduction in the computational times.
In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on Synthetic Aperture Radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In the present paper, an
innovative parametric estimation methodology for SAR amplitude data is proposed, that takes into account the physical nature of the scattering phenomena generating a SAR image by adopting a generalized Gaussian (GG) model for the backscattering phenomena. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed "method-of-log-cumulants" (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions, and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude probability density function better than several previously proposed parametric models for backscattering phenomena.
The use of remotely sensed imagery for environmental monitoring naturally leads to operate with multitemporal images of the geographical area of interest. In order to generate thematic maps for all acquisition dates, an unsupervised classification algorithm is not effective, due to the lack of knowledge about the thematic classes. On the other hand, a detailed analysis of all the land-cover transitions is naturally accomplished in a completely supervised context, but the ground-data requirement involved by this approach is not realistic in case of short rivisit time. An interesting trade-off is represented by the partially supervised approach, exploiting ground truth only for a subset of the acquisition dates. In this context, a multitemporal classification scheme has been proposed previously by the authors, which deals with a couple of images of the same area, assuming ground truth to be available only at the first date. In the present paper, several modifications are proposed to this system in order to automatize it and to improve the detection performances. Specifically, a preprocessing algorithm is developed, which addresses the problem of mismatches in the dynamics of images acquired at different times over the same area, by both automatically correcting strong dynamics differences and detecting cloud areas. In addition, the clustering procedures integrated in the system are fully automatized by optimizing the selection of the numbers of clusters according to Bayesian estimates of the probability of correct classification. Experimental results on multitemporal Landsat-5 TM and ERS-1 SAR data are presented.
A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all thematic classes that are present in the considered data set. However, the training set representing this prior knowledge usually does not really describe all the land cover typologies in the image and the generation of a complete training data set would be a time-consuming, difficult and expensive task. This problem may play a relevant role in remote sensing data analysis, since it affects the classification performances of supervised classifiers, that erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is proposed, which allows the identification of samples of unknown classes, through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines for the estimation of probability density functions and on a recursive procedure to generate prior probabilities estimates for both known and unkown classes. For experimental purposes, both a synthetic and a real data set are considered.
The task of the analysis of hyperspectral data, due to their high spectrla reolution, requires dealing with the problem of the curse of dimenioality. Many feature selection/extraction techniques have been developed, which map the hyperdimensional feature space in a lower-dimensional space, based on the optimization of a suitable criterion function. This paper studies the impact of several such techniques and of the criterion chosen on the accuracy of different supervised classifiers. The compared methods are the 'Sequential Forward Selection' (SFS), the 'Steepest Ascent' (SA), the 'Fast Constrained Search' (FCS), the 'Projection Pursuit' (PP) and the 'Decision Boundary Feature Extraction' (DBFE), while the considered criterion functions are standard interclass distance measures. SFS is well known for its conceptual and computational simplicity. SA provides more effective subsets of selected features at the price of a higher computational cost. DBFE is an effective transformation technque, usually applied after a preliminary feature-space reduction through PP. The experimental comparison is performed on an AVIRIS hyperspectral data set characterized by 220 spectral bands and nine ground cover classes. The computational time of each algorithm is also reported.
An unsupervised change detection problem can be viewed as a classification problem with only two classes corresponding to the change and no-change areas, respectively. Due to its simplicity, image differencing represents a popular approach for change detection. It is based on the idea to generate a difference image that represents the modulus of the spectral change vector associated to each pixel in the study area. To separate the change and no-change classes in the difference image, a simple thresholding-based procedure can be applied. However, the selection of the best threshold value is not a trivial problem. In the present work, several simple thresholding methods are investigated and compared. The combination of the Expectation-Maximization algorithm with a thresholding method is also considered with the aim of achieving a better estimation of the optimal threshold value. For experimental purpose, a study area affected by a forest fire is considered. Two Landsat TM images of the area acquired before and after the event are utilized to reveal the burned zones and to assess and compare the above mentioned unsupervised change detection methods.
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