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In this paper, we propose a solution on microaggregation problem based on the hierarchical tree equi-partition (HTEP) algorithm. Microaggregation is a family of methods for statistical disclosure control of microdata, that is, for masking microdata, so that they can be released without disclose private information on the underlying individuals. Knowing that the microaggregation problem is non-deterministic polynomial-time-hard, the goal is to partitionNgiven data into groups of at leastKitems, so that the sum of the within-partition squared error is minimized. The proposed method is general and it can be applied to any tree partition problem aiming at the minimization of a total score. The method is divisive, so that the tree with the highest ‘score’ is split into two trees, resulting in a hierarchical forest of trees with almost equal ‘score’ (equipartition). We propose a version of HTEP for microaggregation (HTEPM), that is applied on the minimum spanning tree (MST) of the graph defined by the data. The merit of the HTEPM algorithm is that it solves optimally some instances of the multivariate microaggregation problem on MST search space in. Experimental results and comparisons with existing methods from literature prove the high performance and robustness of HTEPM. [ABSTRACT FROM AUTHOR]

We propose a general purpose image segmentation framework, which involves feature extraction and classification in feature space, followed by flooding and merging in spatial domain. Region growing is based on the computed local measurements and distances from the distribution of features describing the different classes. Using the properties of the label dependent distances spatial coherence is ensured, since the image features are described globally. The distribution of the features for the different classes are obtained by block-wise unsupervised clustering based on the construction of the minimum spanning tree of the blocks' grid using the Mallows distance and the equipartition of the resulting tree. The final clustering is obtained by using the k-centroids algorithm. With high probability and under topological constraints, connected components of the maximum likelihood classification map are used to compute a map of initially labelled pixels. An efficient flooding algorithm is introduced, namely, Priority Multi-Class Flooding Algorithm (PMCFA), that assign pixels to labels using Bayesian dissimilarity criteria. A new region merging method, which incorporates boundary information, is introduced for obtaining the final segmentation map. Therefore, the merging stage is based on region features and edge localization. Segmentation results on the Berkeley benchmark data set demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods. [ABSTRACT FROM AUTHOR]

A geophysical investigation was carried across the M3 burial mound from Silvașu de Jos —Dealu Țapului, a tumuli necropolis in western Romania, where the presence of the Yamnaya people was certified archaeologically. For characterizing the inner structure of the mound, two conventional geophysical methods have been used: a geomagnetic survey and electrical resistivity tomography (ERT). The results allowed the mapping of the central features of the mound and the establishment of the relative stratigraphy of the mantle, which indicated at least two chronological phases. Archaeological excavations performed in the central part of the mound accurately validated the non-invasive geophysical survey and offered a valuable chronological record of the long-forgotten archaeological monument. Geophysical approaches proved to be an invaluable instrument for the exploration of the monument and suggest a fast constructive tool for the investigation of the entire necropolis which currently has a number of distinct mounds. [ABSTRACT FROM AUTHOR]

Detailed information regarding land utilization/cover is a valuable resource in various fields. In recent years, remote sensing images, especially aerial images, have become higher in resolution and larger span in time and space, and the phenomenon that the objects in an identical category may yield a different spectrum would lead to the fact that relying on spectral features only is often insufficient to accurately segment the target objects. In convolutional neural networks, down-sampling operations are usually used to extract abstract semantic features, which leads to loss of details and fuzzy edges. To solve these problems, the paper proposes a Multi-level Feature Aggregation Network (MFANet), which is improved in two aspects: deep feature extraction and up-sampling feature fusion. Firstly, the proposed Channel Feature Compression module extracts the deep features and filters the redundant channel information from the backbone to optimize the learned context. Secondly, the proposed Multi-level Feature Aggregation Upsample module nestedly uses the idea that high-level features provide guidance information for low-level features, which is of great significance for positioning the restoration of high-resolution remote sensing images. Finally, the proposed Channel Ladder Refinement module is used to refine the restored high-resolution feature maps. Experimental results show that the proposed method achieves state-of-the-art performance 86.45% mean IOU on LandCover dataset. [ABSTRACT FROM AUTHOR]

Synthetic aperture imaging radiometers (SAIRs) are powerful passive microwave systems for high-resolution imaging by use of synthetic aperture technique. However, the ill-posed inverse problem for SAIRs makes it difficult to reconstruct the high-precision brightness temperature map. The traditional regularization methods add a unique penalty to all the frequency bands of the solution, which may cause the reconstructed result to be too smooth to retain certain features of the original brightness temperature map such as the edge information. In this paper, a multi-parameter regularization method is proposed to reconstruct SAIR brightness temperature distribution. Different from classical single-parameter regularization, the multi-parameter regularization adds multiple different penalties which can exhibit multi-scale characteristics of the original distribution. Multiple regularization parameters are selected by use of the simplified multi-dimensional generalized cross-validation method. The experimental results show that, compared with the conventional total variation, Tikhonov, and band-limited regularization methods, the multi-parameter regularization method can retain more detailed information and better improve the accuracy of the reconstructed brightness temperature distribution, and exhibit superior noise suppression, demonstrating the effectiveness and the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

The automatic extraction of geomorphologic features using digital elevation models (DEMs) belongs to the remote sensing techniques used to improve our understanding in a variety of disciplines in the geosciences (i.e. remote sensing, topography, geology, archaeology, oceanography and others) prior the fieldwork and later during data processing and interpretation. This approach highly contributes towards a rapid, objective and lower cost fieldwork. In the present work, recently developed techniques of pattern recognition in the context of European Projects (NEREIDs, Sea4All), are applied on digital elevation models (DEMs) from onshore and offshore areas of the Mediterranean aiming to a) demonstrate their efficiency, and b) partly compare the results of the automatic detection with geological data provided either from previous researches or ground truth data.Panagiotakis and Kokinou (2014, 2015, and 2017) have implemented the two-step methodology, to process digital elevation models. It was applied partly or in whole in previous onshore and offshore geological and environmental studies. The proposed approach is based on a) the automatic detection of topographic highs and computation of the geomorphologic features (orientation, eccentricity, average slope, and shape complexity) and b) the automatic detection of lineaments, possibly related to the presence of geological faults or other subsurface processes.The two-step methodology has been evaluated on real and synthetic digital elevation models of medium and high accuracy that correspond to onshore and offshore environment. The experimental results demonstrate the high performance of the proposed approach on several real and synthetic topographic data. In our tests major and accompanying geomorphologic structures (lineaments, geological faults, topographic highs and depressions und others) are estimated with high precision concerning their location, shape, and orientation. Furthermore, the financial cost of the fieldwork severely decreases, since prior to the fieldwork geomorphologic structures of interest are identified with great precision and scheduled to be examined. [ABSTRACT FROM AUTHOR]