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In this paper, a new joint spectral–spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence. [ABSTRACT FROM AUTHOR]

Dense stereo image matching in remotely sensed images is a challenging problem, though it has been studied for more than two decades, due to occlusions, discontinuities, geometric, and radiometric distortions. A novel multiobjective fitness function-based dense stereo matching approach using genetic algorithms (GAs) is proposed in this paper. The proposed method is useful for estimating dense disparity map with an improved number of inliers for a stereo image pair, despite the constraint of finding correct disparity at depth discontinuities. In this paper, the steps of GA, such as initialization of the population, fitness function, and crossover and mutation operation, are designed and implemented to effectively deal with the problem of dense stereo image matching. To initialize the population, a Scale Invariant Feature Transform (SIFT) descriptor is computed for each pixel and multiple-size window-based matching is performed, using the similarity measures: 1) Euclidean distance and 2) spectral angle mapper. The generated disparity maps are pruned to choose a suitable subset using the designed fitness functions, considering the constraints related to stereo image pair, such as epipolar constraint, which encodes the epipolar geometry and the similarity measure that is useful to decide accuracy of the correspondences. The two objective functions are the number of inliers computed using the fundamental matrix and an energy minimization function, considering discontinuities and occlusions. The usefulness of this approach for remotely sensed stereo image pairs is demonstrated by improving the number of inliers and favorably comparing with state-of-the-art dense stereo image matching methods. [ABSTRACT FROM AUTHOR]

One-minute rain rate exceeded for 0.01% of an average year (known as R0.01) is an important parameter, which is required in rain fade models for planning both satellite and terrestrial links, especially in the microwave and millimeter-wave bands. This paper presents the development of a new global model for estimating high-resolution R0.01. The model is developed using the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA-Interim reanalyzed total precipitable water, and the cumulative distribution functions of the measured R0.01 via genetic algorithm. The newly proposed model performs better than the International Telecommunication Union Radio Sector (ITU-R) recommendation and widely used ITU-R P.837-6 model, particularly over the tropical regions, where ITU-R P.837-6 strongly underestimates R0.01. Overall, when compared to ITU-R P.837-6, the estimated R0.01 by the proposed model is improved by ~15%. Furthermore, the proposed model can provide R0.01 with a much higher spatial resolution (~5 km) as compared to ITU-R P.837-6 (~110 km). Another key advantage of the proposed model over ITU-R P.837-6 is that the inputs required for this model are readily available from meteorological database as well as from space-based observing system. This paper also investigated the long-term (1981–2016) trends in R0.01. The analysis revealed significant increasing trends in R0.01 over most parts of the globe, India and northern part of Southern America exhibited the strongest ($\sim 0.5\,\,\text {mm}\cdot \text {h}^{-1}\cdot $ year−1) increasing trends. It is anticipated that R0.01 obtained with the newly proposed model will have large implications in rain attenuation modeling for planning both terrestrial and earth–satellite microwave links. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for the development of automatic object-based techniques used for hyperspectral image classification. The proposed approach employs an adaptive smoothing step that utilizes an extension of partial differential equations (PDEs) from real domain (RPDE) to complex domain (CPDE). This idea results in generalized PDEs that simultaneously have the properties of both forward and backward diffusions. The genetic algorithm and an innovative fitness function are applied for adaptively tuning the CPDE parameters. The smoothed data are then fed into a Pixon-based object extraction process, which is itself an adaptive process. We also propose a novel distance metric for the Pixon creation step in order to facilitate the use of textural information which exists in the data. The spectral features of the extracted objects are used for a support vector machine classifier. [ABSTRACT FROM AUTHOR]

This paper proposes to use empirical mode decomposition (EMD) with spectral gradient enhancement to increase the classification accuracy of hyperspectral images with support vector machine (SVM) classification. Recently, it has been shown that higher hyperspectral image classification accuracy can be achieved by using 2-D EMD that is applied to each hyperspectral band separately to obtain the intrinsic mode functions (IMFs) of each band, while the sum of the IMFs are used as feature data in the SVM classification process. In the previous approach, IMFs have been summed directly, i.e., with equal weights. It is shown in this paper, that it is possible to significantly increase the classification accuracy by using appropriate weights for the IMFs in the summation process. In the proposed approach, the weights of the IMFs are obtained so as to optimize the total absolute spectral gradient, and a genetic algorithm-based optimization strategy has been adopted to obtain the weights automatically in this way. While the 2-D EMD basically provides spatial processing, the proposed method further incorporates spectral enhancement into the process. It is shown that a significant increase in hyperspectral image classification accuracy can be achieved using the proposed approach. [ABSTRACT FROM PUBLISHER]

Multiview synthetic aperture radar (SAR) images could provide much richer information for automatic target recognition (ATR) than from a single-view image. It is desirable to find optimal SAR platform flight paths and acquire a sequence of SAR images from appropriate views, so that multiview SAR ATR can be carried out accurately and efficiently. In this paper, a novel optimization framework for multiview SAR ATR is proposed and implemented. The geometry of the multiview SAR ATR is modeled according to the recognition mission and flight environment. Then, the multiview SAR ATR is abstracted and transformed into a constrained multiobjective optimization problem with objective functions considering the tradeoffs between recognition performance and efficiency and security. A specific approach based on convolutional neural network ensemble and constrained nondominated sorting genetic algorithm II is employed to solve the multiobjective optimization, and optimal flight paths and corresponding imaging viewpoints are obtained. The SAR sensor can thus choose an applicable flight path to acquire the multiview SAR images from different tradeoff solutions according to application requirements. Finally, accurate recognition results can be obtained based on those multiview SAR images. Extensive experiments have shown the validity and superiority of the proposed optimization framework of multiview SAR ATR. [ABSTRACT FROM AUTHOR]

Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is usually necessary in order to properly model the spatial information in a remote sensing scene. A dense sampling of the values of the parameters of the filters generates profiles that have both a very large dimensionality (leading to the Hughes phenomenon in classification) and a high redundancy. In this paper, a novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles. The selection of the filtered images that compose the profile is performed by dividing them into three classes corresponding to high, medium, and low importance. We propose to measure the importance (modeled in terms of discriminative power in the classification task) using a random forest classifier, which provides a rank for each feature with its model. Only the set of images associated with the highest importance is selected, i.e., preserved for classification. The proposed technique is applied to the features labeled with medium importance, whereas the images with the lowest importance are removed from the profile. This method is employed to classify three hyperspectral data sets achieving significantly high classification accuracy values. A parallel computing implementation has been developed in order to significantly reduce the time required for the run of the GAs. [ABSTRACT FROM AUTHOR]

Application of polarimetric synthetic aperture radar interferometry to the retrieval of geophysical parameters from vegetated scenes is based on simple direct models of such scenes. The first part of this paper presents an analysis of the correspondence between these simple models, namely, the random volume over ground and the oriented volume over ground (OVoG), and experimental data from samples of two agricultural crops (maize and rice) acquired in controlled conditions. Although an overall agreement between model and data is clear, some discrepancies have been found as a consequence of two assumptions in the model formulation: vertical homogeneity of the vegetation volume and absence of multiple scattering effects inside the volume. This paper presents the shape and location of the visible region of the experimental coherences on the complex plane and compares it with the feasible region predicted by the model. This comparison has also pointed out the low sensitivity of the direct model to extinction coefficients. In the second part, two different strategies for a complete inversion (i.e., estimation of all model parameters) of the OVoG model are proposed and compared, using the same data set. The first one is based on a combination of geometrical and numerical approaches (genetic algorithms) and the second one on a dual-baseline configuration. In all cases, ground topography is accurately estimated, with a maximum error of 10 cm. Vegetation height estimates are accurate up to 30 cm, with some bands and baseline configurations providing errors below 15 cm. However, results obtained for the extinction coefficients are not stable with frequency and exhibit high variability. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for estimating the model parameters of the probability integral method (PIM) based on the line-of-sight deformation derived from the interferometric synthetic aperture radar. Then, it applies the settled PIM to forward predict the horizontal and vertical displacements induced by the extraction of a new working panel. The proposed method first constructed the functional relationship between the InSAR-derived LOS deformation and the model parameters of PIM. Subsequently, an improved genetic algorithm (GA), in which gross error elimination was imposed, was proposed, and used to estimate the model parameters of PIM with a large number of LOS deformation measurements. The estimated model parameters and PIM were then employed to forward predict the horizontal and vertical displacements induced by the extraction of a working panel. Simulated experiments show that the rmses of the predicted displacements along the up–down, west–east, and north–south directions are 1.5, 0.9, and 2.5 mm, respectively. Real data experiments over the Qianyingzi coal mining area of China indicate that the predicted displacements are highly consistent with those by field surveys, with rmses of 4.1 and 3 cm for the vertical and horizontal directions, respectively. These imply that the proposed approach can be a very promising tool for predicting the mining-induced displacements and will potentially contribute to the assessing and forecasting of possible geological hazards in the mining area. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object's extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets. [ABSTRACT FROM PUBLISHER]

Among the multisource data fusing methods, the potential advantages of remote sensing of solar-reflective visible and near-Infrared [(VNIR); 400-900 nm] data and thermal-infrared (TIR) data have not been fully mined. Usually, a linear unmixed method is used for the purpose, which results in low estimation accuracy of subpixel land-surface temperature (LST). In this paper, we propose a novel method to estimate subpixel LST. This approach uses the characteristics of high spatial-resolution advanced spaceborne thermal emission and reflection radiometer (ASTER) VNIR data and the low spatial-resolution TIR data simulated from ASTER temperature product to generate the high spatial-resolution temperature data at a subpixel scale. First, the land-surface parameters (e.g., leaf area index, normalized difference vegetation index (NDVI), soil water content index, and reflectance) were extracted from VNIR data and field measurements. Then, the extracted high resolution of land-surface parameters and the LST were simulated into coarse resolutions. Second, the genetic algorithm and self-organizing feature map artificial neural network (ANN) was utilized to create relationships between land-surface parameters and the corresponding LSTs separately for different land-cover types at coarse spatial-resolution scales. Finally, the ANN-trained relationships were applied in the estimation of subpixel temperatures (at high spatial resolution) from high spatial-resolution land-surface parameters. The two sets of data with different spatial resolutions were simulated using an aggregate resampling algorithm. Experimental results indicate that the accuracy with our method to estimate land-surface subpixel temperature is significantly higher than that with a traditional method that uses the NDVI as an input parameter, and the average error of subpixel temperature is decreased by 2-3 K with our method. This method is a simple and convenient approach to estimate subpixel LST from high spatial-temporal resolution data quickly and effectively. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images. The proposed approach aims at extracting the change information by jointly analyzing the spectral channels of multitemporal images in the original feature space without any training data. This is accomplished by using a selective Bayesian thresholding for deriving a pseudotraining set that is necessary for initializing an adequately defined binary semisupervised support vector machine (S³VM) classifier. Starting from these initial seeds, the S³VM performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a semisupervised learning algorithm. This algorithm models the full complexity of the change-detection problem, which is only partially represented from the seed pixels included in the pseudotraining set. The values of the classifier parameters are then defined according to a novel unsupervised model-selection technique based on a similarity measure between change-detection maps obtained with different settings. Experimental results obtained on different multispectral remote-sensing images confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In January 2006, the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society launched a public contest for pansharpening algorithms, which aimed to identify the ones that perform best. Seven research groups worldwide participated in the contest, testing eight algorithms following different philosophies [component substitution, multiresolution analysis (MRA), detail injection, etc.]. Several complete data sets from two different sensors, namely, QuickBird and simulated Pléiades, were delivered to all participants. The fusion results were collected and evaluated, both visually and objectively. Quantitative results of pansharpening were possible owing to the availability of reference originals obtained either by simulating the data collected from the satellite sensor by means of higher resolution data from an airborne platform, in the case of the Pléiades data, or by first degrading all the available data to a coarser resolution and saving the original as the reference, in the case of the QuickBird data. The evaluation results were presented during the special session on Data Fusion at the 2006 International Geoscience and Remote Sensing Symposium in Denver, and these are discussed in further detail in this paper. Two algorithms outperform all the others, the visual analysis being confirmed by the quantitative evaluation. These two methods share the same philosophy: they basically rely on MRA and employ adaptive models for the injection of high-pass details. [ABSTRACT FROM AUTHOR]

An important approach for unsupervised Iandcover classification in remote sensing images is the clustering of pixels in the spectral domain into several fuzzy partitions. In this paper, a multiobjective optimization algorithm is utilized to tackle the problem of fuzzy partitioning where a number of fuzzy cluster validity indexes are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Real-coded encoding of the cluster centers is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Different landcover regions in remote sensing imagery have also been classified using the proposed technique to establish its efficiency. [ABSTRACT FROM AUTHOR]

The possibility of predicting ocean-surface wind field a few days ahead from satellite scatterometer observations in the Arabian Sea has been explored in this paper. The prediction technique is based on a combination of empirical orthogonal function (EOF) analysis and genetic algorithm (GA). The space-time distributed satellite data (zonal or meridional wind field) have been decomposed into a set of spatial eigenmodes ranked by their temporal variance. The associated temporal amplitude functions have been used by the GA for carrying out forecasts with lead times varying from one to five days. The GA finds the analytical equations that best describe the behavior of the different temporal amplitude functions in the EOF decomposition. Later, the predicted wind field has been generated as a linear combination of the dominant spatial modes weighted by the corresponding predicted amplitudes. The technique has been tested using independent validation data sets. It has been further tested by comparing the forecast fields with buoy data. The performance of GA is comparable to that of persistence forecast for the first two days of forecast, while it is better than that of persistence for three- to five-day-ahead forecasts. [ABSTRACT FROM AUTHOR]

A ground-penetrating radar (GPR) antenna system is modeled using the three-dimensional finite-difference time-domain technique. This paper investigates some basic geometrical shapes for planar dipoles to find what shape gives the best performance for GPR applications. The antenna is resistor loaded and shielded by a rectangular conducting cavity to suit the application. The effect of adding a wave-absorbing coat to the shield is also studied. Furthermore, a genetic algorithm is used to optimize the cavity height and the resistor values. [ABSTRACT FROM AUTHOR]

The intent of this paper is to propose new methods for the reconstruction of areas obscured by clouds. They are based on compressive sensing (CS) theory, which allows finding sparse signal representations in underdetermined linear equation systems. In particular, two common CS solutions are adopted for our reconstruction problem: the basis pursuit and the orthogonal matching pursuit methods. A novel alternative CS solution is also proposed through a formulation within a multiobjective genetic optimization scheme. To illustrate the performances of the proposed methods, a thorough experimental analysis on FORMOsa SATellite-2 and Satellite Pour l'Observation de la Terre-5 multispectral images is reported and discussed. It includes a detailed simulation study that aims at assessing the accuracy of the methods in different qualitative and quantitative cloud-contamination conditions. Compared with state-of-the-art techniques for cloud removal, the proposed methods show a clear superiority, which makes them a promising tool in cleaning images in the presence of clouds. [ABSTRACT FROM AUTHOR]

The deployment of a network of HF surface wave radar (HFSWR) systems is a complex task with many factors to be considered, particularly when the radars are expected to perform multiple roles. Failure to treat the siting problem with appropriate care could seriously degrade performance in one or more radar missions. In this paper, we describe a practical technique for HFSWR network design, based on a genetic algorithm adapted to multi-objective optimization, and demonstrate its efficacy in the context of a hypothetical two-radar system deployed in the Strait of Malacca, a major waterway along which many critical surveillance requirements have been identified. The results confirm that quite disparate criteria can be taken into account with this approach and support our claim that this methodology can be extended to higher dimensions where exhaustive search is completely out of the question. [ABSTRACT FROM PUBLISHER]

This paper proposes the use of a genetic fuzzy-rule-based classification system for land cover classification from hyperspectral images. The proposed classifier, namely, Feature Selective Linguistic Classifier, is constructed through a three-stage learning process. The first stage produces a preliminary fuzzy rule base in an iterative fashion. During this stage, a local feature selection scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the relevance of each feature with respect to its classification ability. The structure of the model is then simplified in a subsequent postprocessing stage. The performance of the classifier is finally optimized through a genetic tuning stage. An extensive comparative analysis, using an Earth Observing-1 Hyperion satellite image, highlights the quality advantages of the proposed system, when compared with nonfuzzy classifiers, commonly employed in hyperspectral classification tasks. [ABSTRACT FROM AUTHOR]

To design a highly automatic and practical method for target discrimination in synthetic aperture radar images, we propose in this paper an improved scheme consisting of the framework and algorithms for target discrimination. Our main contribution in this scheme comprises four aspects. First, an integrative frame sequentially combining the algorithm based on feature extraction and the knowledge of target group has been presented. Second, three new features for target discrimination have been introduced. Third, a genetic algorithm-based feature-selection algorithm has been presented. The results show that this algorithm can evaluate the goodness-of-feature better. Finally, to improve the accuracy of the discriminator, we have designed a weighted quadratic distance discriminator, which has been observed to improve the performance of target discrimination. We have analyzed the performance of the proposed scheme comprehensively and specifically using some measured data, and carried out comparisons of the existing algorithms. The results show that the proposed scheme could improve the application ability in target discrimination. [ABSTRACT FROM AUTHOR]

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types, some of which cover significantly large areas while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper, a new real-coded modified differential evolution based automatic fuzzy clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Here, the assignment of points to different clusters is done based on a Xie–Beni index where the Euclidean distance is taken into consideration. The effectiveness of the proposed technique is first demonstrated for two numeric remote sensing data described in terms of feature vectors and then in identifying different landcover regions in remote sensing imagery. The superiority of the new method is demonstrated by comparing it with other existing techniques like automatic clustering using improved differential evolution, classical differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering, and well known fuzzy C-means algorithm both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

A new parametric autofocus technique with a high accuracy of flight-parameter estimation dedicated to strip-mode synthetic aperture radar (SAR) systems is presented. Most of the known autofocus techniques require high-reflectivity targets (man-made targets) to obtain a properly focused SAR image. The technique proposed in this paper allows flight parameters to be estimated effectively, even for a low-contrast scene (e.g., forests, fields, small paths, etc.). The autofocus technique is based on well-known MapDrift (MD) principles. The presented technique is a coherent one, which allows flight parameters to be estimated more precisely than in the other well-known parametric technique referred to as classical MD. The presented technique allows flight parameters to be estimated with accuracy that is independent of the initial velocity error. It can be used for real-time processing for both Earth imaging and moving-target indication. [ABSTRACT FROM AUTHOR]

In this paper, a novel method for semisupervised classification with limited training samples is presented. Its aim is to exploit unlabeled data available at zero cost in the image under analysis for improving the accuracy of a classification process based on support vector machines (SVMs). It is based on the idea to augment the original set of training samples with a set of unlabeled samples after estimating their label. The label estimation process is performed within a multiobjective genetic optimization framework where each chromosome of the evolving population encodes the label estimates as well as the SVM classifier parameters for tackling the model selection issue. Such a process is guided by the joint minimization of two different criteria which express the generalization capability of the SVM classifier. The two explored criteria are an empirical risk measure and an indicator of the classification model sparseness, respectively. The experimental results obtained on two multisource remote sensing data sets confirm the promising capabilities of the proposed approach, which allows the following: 1) taking a clear advantage in terms of classification accuracy from unlabeled samples used for inflating the original training set and 2) solving automatically the tricky model selection issue. [ABSTRACT FROM AUTHOR]

A novel Self-Organizing Neuro-Fuzzy Multilayered Classifier, the GA-SONeFMUC model, is proposed in this paper for land cover classification of multispectral images. The model is, composed of generic fuzzy neuron classifiers (FNCs) arranged in layers, which are implemented by fuzzy rule-based systems. At each layer, parent FNCs are combined to generate a descendant FNC at the next layer with higher classification accuracy. To exploit the information acquired by the parent FNCs, their decision supports are combined using a fusion operator. As a result, a data splitting is devised within each FNC, distinguishing those pixels that are currently correctly classified to a high certainty grade from the ambiguous ones. The former are handled by the fuser, while the ambiguous pixels are further processed to enhance their classification confidence. The GA-SONeFMUC structure is determined in a self-constructing way via a structure-learning algorithm with feature selection capabilities. The parameters of the models obtained after structure learning are optimized using a real-coded genetic algorithm. For effective classification, we formulated three input sets containing spectral and textural feature types. To explore information coming from different feature sources, we apply a classifier fusion approach at the final stage. The outputs of individual classifiers constructed from each input set are combined to provide the final assignments. Our approach is tested on a lake-wetland ecosystem of international importance using an IKONOS image. A high-classification performance of 92.02% and of 75.55% for the wetland zone and the surrounding agricultural zone is achieved, respectively. [ABSTRACT FROM AUTHOR]

Monitoring of water consumption in irrigation systems has become increasingly important for water managers in the actual trend of integrated water management. Low spatial resolution (LSR) satellite remote sensing has already proven the capacity of monitoring evapotranspiration (ETa) over large areas at high temporal frequencies, by which monitoring for large irrigation systems can be satisfied. However, smaller pixel size is still required for more local management while keeping the return period within a few days. High spatial resolution (HSR) satellite imagery is indeed available for calculation of ETa and has already been used in many studies. However, its practical return period is a major drawback to its implementation for monitoring irrigation systems. This paper is perusing into the use of genetic algorithms to assimilate parameters of an agrohydrological model called soil-water-air-plant for each of the pixels of HSR images contained into one single pixel of an LSR multitemporal image. The methodology developed and experimented here is trying to take advantage of the spatial content of HSR images and the temporal content of LSR images by fusing them by the process of data assimilation. [ABSTRACT FROM AUTHOR]

Recent remote sensing literature has shown that support vector machine (SVM) methods generally outperform traditional statistical and neural methods in classification problems involving hyperspectral images. However, there are still open issues that, if suitably addressed, could allow further improvement of their performances in terms of classification accuracy. Two especially critical issues are: 1) the determination of the most appropriate feature subspace where to carry out the classification task and 2) model selection. In this paper, these two issues are addressed through a classification system that optimizes the SVM classifier accuracy for this kind of imagery. This system is based on a genetic optimization framework formulated in such a way as to detect the best discriminative features without requiring the a priori setting of their number by the user and to estimate the best SVM parameters (i.e., regularization and kernel parameters) in a completely automatic way. For these purposes, it exploits fitness criteria intrinsically related to the generalization capabilities of SVM classifiers. In particular, two criteria are explored, namely: 1) the simple support vector count and 2) the radius margin bound. The effectiveness of the proposed classification system in general and of these two criteria in particular is assessed both by simulated and real experiments. In addition, a comparison with classification approaches based on three different feature selection methods is reported, i.e., the steepest ascent (SA) algorithm and two other methods explicitly developed for SVM classifiers, namely: 1) the recursive feature elimination technique and 2) the radius margin bound minimization method. [ABSTRACT FROM AUTHOR]

Several computational intelligence components, namely neural networks (NNs), fuzzy sets, and genetic algorithms (GAs), have been applied separately or in combination to the process of remotely sensed data classification. By applying computational intelligence, we expect increased accuracy through the use of NNs, optimal NN structure and parameter determination via GAs, and transparency using fuzzy sets is expected. This paper systematically reviews and compares several configurations in the particular context of remote sensing for land cover. In addition, some of the configurations used here, such as NEFCASS and CANFIS, have few previous applications in the field. A comparison of the configurations is achieved by testing the different methods with exactly the same case-study data. A thorough assessment of results is performed by constructing an accuracy matrix for each training and testing data set. The evaluation of different methods is not only based on accuracy but also on compactness, completeness, and consistency. The architecture, produced rule set, and training parameters for the specific classification task are presented. Some comments and directions for future work are given. [ABSTRACT FROM AUTHOR]

The accuracy of a migration image of ground-penetrating radar (GPR) depends strongly on the accuracy of permittivity distribution determined from multioffset data. This paper proposes a migration velocity analysis method using a genetic algorithm (GA). The objective function is defined as the summation of normalized zero-delay cross correlation of all common-image point gathers. Under the assumptions that the media are blockwise and that the permittivity of each block can be expressed as a polynomial with limited terms, all coefficients of the permittivity function of each block, which maximize the objective function, are deter- mined by migration velocity analysis method with GA Prestack migration is performed by a reverse-time migration method based on Maxwell's equations solved by the finite-difference time-domain method with a perfectly matched layer absorbing boundary conditions. The migration velocity analysis method is applied to synthetic common-transmitter datasets to test the method. Then, the velocity analysis and prestack migration method are applied to field data. From the distribution of dielectric constant obtained from the field data, water content is derived, and the depth of a water aquifer is deduced from the water content distribution and a migration stack profile. [ABSTRACT FROM AUTHOR]

In the above named paper [ibid., vol. 49, no. 1, pp. 277-294, Jan. 2011], there is an error in equation (13). The corrected equation is given here. [ABSTRACT FROM AUTHOR]

A numerical technique based on genetic algorithms (GAs) is used to invert the equations of an electromagnetic model based on dense-medium radiative transfer theory (DMRT) to retrieve snow depth, mean grain size, and fractional volume from microwave brightness temperatures. In order to study the sensitivity of the GA to its parameters, the technique is initially tested on simulated microwave data with and without adding a random noise. A configuration of GA parameters is selected and used for the retrieval of snow parameters from both ground-based observations and brightness temperatures recorded by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Retrieved snow parameters are then compared with those measured on ground. Although more investigation is required, results suggest that the proposed technique is able to retrieve snow parameters with good accuracy. [ABSTRACT FROM AUTHOR]

Presents a technique for the detection of roads in a spaceborne synthetic aperture radar images using a genetic algorithm. Discussion on curvilinear structures and initial grouping; Information on grouping by the genetic algorithm; Uses of the road detection method.

Climate change is altering the water-table (WT) height and near-surface moisture conditions in northern peatlands, which in turn both increases the susceptibility to fire and reduces the carbon sink capacity of these ecosystems. To further develop remote sensing-based measurements of peatland moisture characteristics, we employed coincident surface reflectance and moisture measurements in two Sphagnum moss-dominated peatland sites. We applied the Mexican hat continuous wavelet transform to the measured spectra to generate wavelet features and coefficients across a range of scales. Overall, wavelet analysis was an improvement over the previously tested spectral indices at both the study sites. Linear mixed effect models for WT height using wavelet features accounted for more of the variance with both an improved marginal R^\mathrm 2 (29% greater) and a larger conditional R^\mathrm 2 (21% greater) compared to the best performing spectral index. While spectral indices performed similarly with wavelet coefficients for moisture content measured at 3 cm depth, they performed poorly for volumetric moisture content measured at 7 cm depth. The current study also revealed the advantage of selecting the best subsets of wavelet features based upon genetic algorithm over a more widely used technique that selects features based on correlation scalograms. It also provided new insights into the significance of various spectral regions to detect WT alteration-induced vegetation change. [ABSTRACT FROM PUBLISHER]

We present a coastal ocean current forecasting system using exclusively past observations of a high-frequency radar (HF-Radar). The forecast is made by developing a new approach based on physical and mathematical results of the nonlinear dynamical systems theory that allows to obtain a predictive equation for the currents. Using radial velocities from two HF-Radar stations, the spatiotemporal variability of the fields is first decomposed using the empirical orthogonal functions. The amplitudes of the most relevant modes representing their temporal evolution are then approximated with functions obtained through a genetic algorithm. These functions will be then combined to obtain the hourly currents at the area for the next 36 h. The results indicate that after 4 h and for a horizon of 24 h, the computed predictions provide more accurate current fields than the latest available field (i.e., persistent field). [ABSTRACT FROM AUTHOR]

Lake ice under phase transition shows large variation on radar backscattering due to the changes of dielectric constant and roughness of ice surface and thus the transmissivity of microwave into ice body. To study the effects of freezing/thawing of ice on radar backscattering in a short time, we spread water over lake ice and continuously measured radar backscattering by using a ground-based microwave scatterometer system operated in C-band HH polarization. By establishing scattering models and applying inversion from genetic algorithm, radar returns were separated into ice-surface, volume, and ice-bottom scatterings, and the changes in dielectric constant and roughness parameters of ice surface were estimated as well. Immediately after spreading water on ice surface, ice-surface scattering was strongest due to high dielectric constant of surface water while volume and ice-bottom scatterings were very weak due to low microwave transmissivity into ice body. As surface water was being frozen, ice-surface scattering became weak with decreasing dielectric constant while volume and ice-bottom scattering increased due to higher transmissivity into ice body. In a transition stage, when surface water was almost frozen, all three scatterings increased simultaneously. Crystallization of ice produced rougher surface overcoming the decrease in dielectric constant, resulting in the increase of ice-surface scattering, while volume and ice-bottom scattering was continuously increased due to increasing transmissivity. At the end of the experiment, air temperature rose above freezing point, and ice surface thawed again so that ice-surface scattering increased while volume and ice-bottom scattering were decreased. [ABSTRACT FROM PUBLISHER]

A new method, called genetic programming-spectral vegetation index (GP-SVI), for the extraction of information from hyperspectral data is presented. This method is introduced in the context of precision farming. GP-SVI derives a regression model describing a specific crop biophysical variable from hyperspectral images (verified with in situ observations). GP-SVI performed better than other methods [multiple regression, tree-based modeling, and genetic algorithm-partial least squares (GA-PLS)] on the task of correlating canopy nitrogen content in a cornfield with pixel reflectance. It is also shown that the band selection performed by GP-SVI is comparable with the selection performed by GA-PLS, a method that is specifically designed to deal with hyperspectral data. [ABSTRACT FROM AUTHOR]

Inversion of ocean color reflectance measurements can be cast as an optimization problem, where particular parameters of a forward model are optimized in order to make the forward-modeled spectral reflectance match the spectral reflectance of a given in situ sample. Here, a simulated ocean color dataset is used to test the capability of a recently introduced global optimization process, particle swarm optimization (PSO), in the retrieval of optical properties from ocean color. The performance of the PSO method was compared with the more common genetic algorithms (GA) in terms of model accuracy and computation time. The PSO method has been shown to outperform the GA in terms of model error. Of particular importance to ocean color remote sensing is the speed advantage that PSO affords over GA. [ABSTRACT FROM AUTHOR]

Phaseless data are used to evaluate the application of an electromagnetic inverse-scattering-based procedure for the detection of cylindrical inhomogeneities, which are schematized as multilayer infinite dielectric cylinders with elliptic cross sections. The electromagnetic inverse problem is recast as a global optimization problem and iteratively solved by an efficient memetic algorithm, which combines deterministic and stochastic concepts. Moreover, a recursive analytical procedure is used for the forward-scattering computation. The possibility of localizing and reconstructing the scatterers by using phaseless input data, which would greatly simplify the design of the imaging apparatus, is evaluated both with reference to synthetically produced data and by means of experimental data obtained by a microwave tomograph. [ABSTRACT FROM AUTHOR]

Deals with a study which discussed the electromagnetic inverse scattering of two-dimensional perfectly conducting objects by real-coded genetic algorithm. Formulation of the problem; Genetic algorithm used; Numerical results; Conclusions.

Presents a study which discussed the concept of chromosome differentiation using variable string genetic algorithms (GA). Effectiveness of a GA-based classifier called the variable string length GA with chromosome differentiation (VGACD)-classifier; Description of VGACD; Establishment merits system through the use of several quantitative measure.

Proposes a computation approach based on genetic algorithm for the solution of a nonlinear inverse scattering problem in microwave imaging applications. Formulation of the inverse scattering problem; Features of genetic algorithms (GA); Disadvantages of GA approach in microwave imaging problem; Numerical results.