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For detailed hydraulic modeling, accurate spatial information of riparian vegetation patterns needs to be derived in automatic fashion. We propose a supervised classification for heterogeneous riparian corridors with a low number of spectrally separate classes using data fusion of a Quickbird image and LIDAR data. The approach considers nine land cover classes including three woody riparian species, brush, cultivated areas, grassland, urban infrastructures, bare soil and water. The classical 'stacked vector' approach is adopted for data fusion, while the nonparametric weighted feature-extraction method and the pixel-oriented maximum likelihood algorithm are used for feature-reduction and classification purposes, respectively. We test the approach over a 14-km stretch of the Sieve River (Tuscany Region, Italy). A one-dimensional river modeling is applied over the study reach comparing the results of a classification- derived hydraulic roughness map and a traditional ground-based approach. Despite the complex study reach, the classification method produced encouraging accuracies (OKS = 0.77) and represents a useful tool to delineate application domains of flow resistance models suited to different hydrodynamic patterns (e.g., stiff/flexible vegetation). Hydraulic modeling results showed that the remotely derived floodplain roughness parameterization captures the equivalent Manning coefficient over 20 test cross sections with uncertainty distributions described by low mean and standard deviation values. DOI: 10.1061/(ASCE)HY.1943-7900.0000254 CE Database subject headings: Flow resistance; Hydrodynamics; Hydrologic models; Remote sensing; Vegetation; Hydraulic roughness. Author keywords: Flow resistance; Manning; Quickbird; LIDAR; Hydrodynamic modeling; Remote sensing.

In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared with single-channel SAR. However, exploiting this potential requires accurate and automatic techniques to generate change maps from (multichannel) SAR images acquired over the same geographic region in different polarizations or at different frequencies at different times. In this paper, a contextual unsupervised change-detection technique (based on a data-fusion approach) is proposed for two-date multichannel SAR images. Each SAR channel is modeled as a distinct information source, and a Markovian approach to data fusion is adopted. A Markov random field model is introduced that combines together the information conveyed by each SAR channel and the spatial contextual information concerning the correlation among neighboring pixels and formulated by using 'energy functions.' In order to address the task of the estimation of the model parameters, the expectation--maximization algorithm is combined with the recently proposed 'method of log-cumulants.' The proposed technique was experimentally validated with semisimulated multipolarization and multifrequency data and with real SIR-C/XSAR images. Index Terms--Change detection, data fusion, expectation--maximization (EM), Markov random fields (MRFs), multichannel SAR, synthetic aperture radar (SAR).

Land surface temperature (LST) and sea surface temperature (SST) are important quantities for many environmental models. Remote sensing is a source of information for their estimation on both regional and global scales. Many algorithms have been devised to estimate LST and SST from satellite data, most of which require a priori information about the surface and the atmosphere. 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 subsequently be used to estimate unknown surface temperatures from 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 quasi-optimal values in the sense of minimum estimation errors. This is achieved by minimizing a functional correlated to regression errors (i.e., the 'span-bound' upper bound on the leave-one-out (LOO) error) which can be computed by using only the training set, without need for a further validation set. In order to minimize this functional, Powell's algorithm is adopted, since it is applicable also to nondifferentiable functions. Experimental results yielded by the proposed method are similar in accuracy to those achieved by cross-validation and by a grid search for the parameter configuration which yields the best test-set accuracy. However, the proposed method gives a dramatic reduction in the computational time required, particularly when many training samples are available. Index Terms--Generalization error bounds, land surface temperature (LST), Powell's method, sea surface temperature (SST), supervised regression, support vector machines (SVMs).

This paper proposes a procedure to extract spectral channels of variable handwidths and spectral positions from the hyperspectral image in such a way as to optimize the accuracy for a specific classification problem. In particular each spectral channel (s-band') is obtained by averaging a group of contiguous channels of the hyperspectral image (h-bands'). Therefore, if one wants to define m s-bands, the problem can be formulated as the optimization of the related m starting and m ending h-bands. Toward this end, we propose to adopt, as an optimization criterion. an interclass distance computed on a training set and to generate a sequence of possible solutions by one of three possible search strategies. As the proposed formalization of the problem makes it analogous to a feature-selection problem, the proposed three strategies have been derived by modifying three feature-selection strategies, namely 1) the ''sequential forward selection.' 2) the ''steepest ascent.' and 3) the 'fast constrained search.' Experimental results on a well-known hyperspectral data set confirm the effectiveness of the approach, which yields better results than other widely used methods. The importance of this kind of procedure lies in feature reduction for hyperspectral image classification or in the ease-based design of the spectral hands of a programmable sensor. It represents a special case of feature extraction that is expected to be more powerful than feature selection. The kind of transformation used allows the interpretability of the new features (i.e., the spectral bands) to be saved. Index Terms--Feature extraction, feature reduction, hyperspectral images, remote-sensing image classification, spectral channels.

In the context of remote-sensing image analysis, Markov random field (MRF) models play an important role, due to their ability to integrate the use of contextual information associated with the image data in the analysis process, through the definition of suitable energy functions. However, especially when dealing with MRF models for supervised classification, the estimation of the parameters characterizing an MRF model is still an open issue, which is typically addressed by using time-expensive interactive 'trial-and-error' procedures. In this paper, by focusing on a broad class of MRF models, in which the energy functions can be expressed as linear combinations of distinct contributions (e.g., representing different typologies of contextual interactions), an automatic supervised procedure for the optimization of the weight parameters of the combinations is proposed. Formulating the parameter-estimation problem as the solution of a system of linear inequalities, the problem is solved by extending to the present context the Ho-Kashyap algorithm, which is typically applied by the pattern-recognition community in computing linear discriminant functions for binary classification. The method is validated experimentally on three different (both single-date and multitemporal) datasets, which are endowed with distinct MRF models formalizing the spatial contextual information associated with a single remote-sensing image and/or the spatio-temporal contextual information associated with an image sequence. Index Terms--Automatic parameter setting, Ho-Kashyap (HK) algorithm, iterated conditional mode (ICM), Markov random field (MRF), remote-sensing image classification.

The availability of synthetic aperture radar (SAR) data offers great potential for environmental monitoring due to the insensitiveness of SAR imagery to atmospheric and sunlight-illumination conditions. In addition, the short revisit time provided by future SAR-based missions will allow a huge amount of multitemporal SAR data to become systematically available for monitoring applications. In this paper, the problem of detecting the changes that occurred on the ground by analyzing SAR imagery is addressed by a completely unsupervised approach, i.e., by developing an automatic thresholding technique. The image-ratioing approach to SAR change detection is adopted, and the Kittler and Illingworth minimum-error thresholding algorithm is generalized to take into account the non-Gaussian distribution of the amplitude values of SAR images. In particular, a SAR-specific parametric modeling approach for the ratio image is proposed and integrated into the thresholding process. Experimental results, which confirm the accuracy of the method for real X-band SAR and spaceborne imaging radar C-band images, are presented. Index Terms--Change detection, Kittler and Illingworth method, method of log-cumulants (MoLC), parametric density estimation, synthetic aperture radar (SAR), thresholding.

A distinct parametric estimation technique for synthetic aperture radar (SAR) amplitude data that assumes a Generalized Gaussian (GG) model for complex SAR backscattered signals is described. The tests show that the method-of-log cumulants (MoLC) estimates are acceptable, consistent, and the amplitude probability density function (PDF) is better than other proposed models.

In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of probability density function (pdf) estimation in the context of synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic models for the pdfs of SAR data have been proposed in the literature, which have been proved to be effective for different land-cover typologies, thus making the choice of a single optimal parametric pdf a hard task, especially when dealing with heterogeneous SAR data. In this paper, an innovative estimation algorithm is described, which faces such a problem by adopting a finite mixture model for the amplitude pdf, with mixture components belonging to a given dictionary of SAR-specific pdfs. The proposed method automatically integrates the procedures of selection of the optimal model for each component, of parameter estimation, and of optimization of the number of components by combining the stochastic expectation-maximization iterative methodology with the recently developed 'method-of-log-cumulants' for parametric pdf estimation in the case of nonnegative random variables. Experimental results on several real SAR images are reported, showing that the proposed method accurately models the statistics of SAR amplitude data. Index Terms--Finite mixture models (FMMs), parametric estimation, probability density function (pdf) estimation, stochastic expectation maximization (SEM), synthetic aperture radar (SAR) images.

A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies iii the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used. Index Terms--Probability density function (pdf) estimation, supervised classification, support vector machines (SVMs), unknown classes.