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Combining various viewpoints to produce coherent and cohesive results requires decision fusion. These methodologies are essential for synthesizing data from multiple sensors in remote sensing classification in order to make conclusive decisions. Using fully polarimetric Synthetic Aperture Radar (PolSAR) imagery, our study combines the benefits of both approaches for detection by extracting Pauli's and Krogager's decomposition components. The Local Pattern Differences (LPD) method was employed on every decomposition component for pixel-level texture feature extraction. These extracted features were utilized to train three independent classifiers. Ultimately, these findings were handled as independent decisions for each land cover type and were fused together using a decision fusion rule to produce complete and enhanced classification results. As part of our approach, after a thorough examination, the most appropriate classifiers and decision rules were exploited, as well as the mathematical foundations required for effective decision fusion. Incorporating qualitative and quantitative information into the decision fusion process ensures robust and reliable classification results. The innovation of our approach lies in the dual use of decomposition methods and the application of a simple but effective decision fusion strategy. [ABSTRACT FROM AUTHOR]

Fusion of hyperspectral data is proposed by means of partitioning the hyperspectral bands into subgroups, prior to principal components transformation (PCT). The first principal component of each subgroup is employed for image visualization. The proposed approach is general, with the number of bands in each subgroup being application dependent. Nevertheless, the paper focuses on partitions with three subgroups suitable for RGB representation. One of them employs matched-filtering based on the spectral characteristics of various materials and is very promising for classification purposes. The information content of the hyperspectral bands as well as the quality of the obtained RGB images are quantitatively assessed using measures such as the correlation coefficient, the entropy, and the maximum energy--minimum correlation index. The classification performance of the proposed partitioning approaches is tested using the K-means algorithm. Index Terms--Color representation, hyperspectral data fusion, image classification, principal components transformation (PCT).

Optimal, in the maximum likelihood sense, constant false-alarm rate (CFAR) detection for Weibull clutter statistics, is investigated. The proposed OW (optimal Weibull) estimator is proved to be an asymptotically efficient estimator of the mean power of the Weibull clutter. Theoretical analysis of the OW-CFAR detector is provided, while detection performance analysis is carried out using the Monte Carlo simulation method. The operation of the median and morphological (MEMO)-CFAR detector in Weibull clutter statistics is also explained. It performs almost optimally in uniform clutter and, simultaneously, it is robust in multitarget situations. The performance of the proposed OW-CFAR detector in uniform Weibull clutter is used as a yardstick in the analysis of the MEMO cell-averager (CA) and ordered statistic (OS)-CFAR detectors. Nonfluctuating and fluctuating (Swerling II) targets are considered in detection analysis. The performance of the detectors is also examined at clutter edges.

In this paper, the issue of employing polarimetric processing methods for persistent scatterers (PS) selection is addressed. The scope of the paper regards the processing methods for PS generation by means of coherent target decomposition (CTD). The key is to exploit scatterer physical characteristics, besides scatterer coherence and stability, so as to classify them as PS utilizing as less data sets as possible. Cameron’s CTD is proposed for PS classification based on scatterers physical properties as an alternative to polarization phase difference (PPD) for it can eliminate the scatterer initial orientation angle. Additionally, provides a complete set of elemental scattering mechanisms in order to describe the physical properties of the scatterer, and thus create a more reliable PS net. These assets make Cameron’s CTD outperform PPD in exploiting scatterer physical properties for PS selection. The study also focuses on minimizing the number of used data sets for PS net generation, which led to experimenting with only two fully polarimetric SAR data sets. The results are promising enough to establish the proposed method as an auxiliary tool to be utilized prior to any classic interferometric techniques for early scene evaluation in order to decide whether the scene would be fruitful for applying permanent scatterer interferometry or not. [ABSTRACT FROM AUTHOR]

In this work, facial thermal infrared images are employed for intoxicated person discrimination. The activity of the facial blood vessels of sober and intoxicated people comes into sight when nonlinear anisotropic diffusion and top-hat transformation are applied to enhance and isolate the vessels from the rest of the information on the face. For an intoxicated person, vessels around the nose and eyes as well as on the forehead become more active, whereas for a person who is sober the vessels’ activity is smoother all over the face’s thermal image. Accordingly, drunkenness can be ascertained by only using the thermal infrared image of the intoxicated person. The proposed method can be incorporated into a non-invasive inspection system used by the police as a first step for intoxicated person detection. The Student’s t-test is employed to assess the degree of confidence in separating the thermal images corresponding to sober and intoxicated people. The 40 participants in the experiment have contributed to the creation of the unique sober–drunk database available on the web. [ABSTRACT FROM PUBLISHER]