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ABSTRACTIn 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.

Drunk person identification is carried out using thermal infrared images. Two different approaches are proposed for distinguishing a drunk person by means of radiometric values on its face. The features used in the first approach are simply the pixel values of specific points on the face of the person. It is proved that the cluster of a specific person moves in the feature space as the person consumes alcohol. Fisher linear discriminant approach is used for space dimensionality reduction. The feature space is found to be of very low dimensionality. The majority of the clusters moves towards the same direction and the feature space can easily be separated into the ‘sober’ and ‘drunk’ regions. Thus the ‘drunk’ feature space is introduced. In the second approach thermal differences between various locations on the face are evaluated and their value is monitored. It was found that specific areas in the face of a drunk person present an increased thermal illumination. These areas are the best candidates for identifying a drunk person. The concept behind this second proposed approach relies on a physiology-based face identification procedure.

The problem of orthogonalization of multiple satellite images, focusing at the same field of view, is studied in this paper for constant false alarm rate (CFAR) point target detection. Information fusion techniques are incorporated. The Karhunen-Loe´ve (K-L) orthogonalization method offers the best CFAR detection performance. However, it requires central fusion. It is shown that a version of the Gram-Schmidt (G-S) orthogonalization method, which preprocesses data in a pipeline form, offers a comparable CFAR detection to that of the K-L method. Point target CFAR detection is carried out by employing various fusion approaches on the orthogonal data. Sensor level fusion with quality information is shown to be preferable when the proposed sequential G-S orthogonalization is applied. The proposed CFAR approach is applied to dissimilar sensors and avoids overloading the communication channel transmitting only in the case of target detection. Trade-off studies and experimental results on real and simulated data are presented. © 1998 Society of Photo-Optical Instrumentation Engineers.

The objective evaluation of the performance of pixel level fusion methods is addressed. For this purpose a global measure based on information theory is proposed. The measure employs mutual and conditional mutual information to assess and represent the amount of information transferred from the source images to the final fused gray-scale image. Accordingly, the common information contained in the source images is considered only once in the performance evaluation procedure. The experimental results clarify the applicability of the proposed measure in comparing different fusion methods or in optimizing the parameters of a specific algorithm.

The Kullback-Leibler (KL) divergence, which is a fundamental concept in information theory used to quantify probability density differences, is employed in assessing the color content of digital images. For this purpose, digital images are encoded in the CIELAB color space and modeled as discrete random fields, which are assumed to be described sufficiently by 3-D probability density functions. Subsequently, using the KL divergence, a global quality assessment of an image is presented as the information content of the CIELAB encoding of the image relative to channel capacity. This is expressed by an image with “maximum realizable color information” (MRCI), which we define. Additionally, 1-D estimates of the marginal distributions in luminance, chroma, and hue are explored, and the proposed quality assessment is examined relative to KL divergences based on these distributions. The proposed measure is tested using various color images, pseudocolor representations and different renderings of the same scene. Test images and a MATLAB implementation of the measure are available online at http://www.ellab.physics.upatras.gr/PersonalPages/VTsagaris/research.htm.