A Review on PolSAR Decompositions for Feature Extraction.

Feature extraction plays a pivotal role in processing remote sensing datasets, especially in the realm of fully polarimetric data. This review investigates a variety of polarimetric decomposition techniques aimed at extracting comprehensive information from polarimetric imagery. These techniques are categorized as coherent and non-coherent methods, depending on their assumptions about the distribution of information among polarimetric cells. The review explores well-established and innovative approaches in polarimetric decomposition within both categories. It begins with a thorough examination of the foundational Pauli decomposition, a key algorithm in this field. Within the coherent category, the Cameron target decomposition is extensively explored, shedding light on its underlying principles. Transitioning to the non-coherent domain, the review investigates the Freeman-Durden decomposition and its extension, the Yamaguchi's approach. Additionally, the widely recognized eigenvector-eigenvalue decomposition introduced by Cloude and Pottier is scrutinized. Furthermore, each method undergoes experimental testing on the benchmark dataset of the broader Vancouver area, offering a robust analysis of their efficacy. The primary objective of this review is to systematically present well-established polarimetric decomposition algorithms, elucidating the underlying mathematical foundations of each. The aim is to facilitate a profound understanding of these approaches, coupled with insights into potential combinations for diverse applications.

Correlated Decision Fusion Accompanied with Quality Information on a Multi-Band Pixel Basis for Land Cover Classification.

Decision fusion plays a crucial role in achieving a cohesive and unified outcome by merging diverse perspectives. Within the realm of remote sensing classification, these methodologies become indispensable when synthesizing data from multiple sensors to arrive at conclusive decisions. In our study, we leverage fully Polarimetric Synthetic Aperture Radar (PolSAR) and thermal infrared data to establish distinct decisions for each pixel pertaining to its land cover classification. To enhance the classification process, we employ Pauli's decomposition components and land surface temperature as features. This approach facilitates the extraction of local decisions for each pixel, which are subsequently integrated through majority voting to form a comprehensive global decision for each land cover type. Furthermore, we investigate the correlation between corresponding pixels in the data from each sensor, aiming to achieve pixel-level correlated decision fusion at the fusion center. Our methodology entails a thorough exploration of the employed classifiers, coupled with the mathematical foundations necessary for the fusion of correlated decisions. Quality information is integrated into the decision fusion process, ensuring a comprehensive and robust classification outcome. The novelty of the method is its simplicity in the number of features used as well as the simple way of fusing decisions.

Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification-A Review.

According to existing signatures for various kinds of land cover coming from different spectral bands, i.e., optical, thermal infrared and PolSAR, it is possible to infer about the land cover type having a single decision from each of the spectral bands. Fusing these decisions, it is possible to radically improve the reliability of the decision regarding each pixel, taking into consideration the correlation of the individual decisions of the specific pixel as well as additional information transferred from the pixels' neighborhood. Different remotely sensed data contribute their own information regarding the characteristics of the materials lying in each separate pixel. Hyperspectral and multispectral images give analytic information regarding the reflectance of each pixel in a very detailed manner. Thermal infrared images give valuable information regarding the temperature of the surface covered by each pixel, which is very important for recording thermal locations in urban regions. Finally, SAR data provide structural and electrical characteristics of each pixel. Combining information from some of these sources further improves the capability for reliable categorization of each pixel. The necessary mathematical background regarding pixel-based classification and decision fusion methods is analytically presented.

Holmium: Yttrium-aluminum-garnet laser lithotripsy: Is there a difference in ablation rates between short and long pulse duration?

Introduction: The high-power holmium: yttrium-aluminum-garnet lasers provide a wide variety of settings for stone disintegration. The aim of this in vitro study is to evaluate the effect of short and long pulse duration on ablation rates on urinary stones. Materials and Methods: Two types of artificial stones were created by BegoStone™ with different compositions (15:3 and 15:6, stone/water ratio). Stones with a 15:3 and 15:6 powder-to-water ratio were defined as hard and soft stones, respectively. Lithotripsy was performed with different laser settings using a custom-made in vitro model consisting of a 60 cm long and 19 mm diameter tube. The ablation rate is defined as the final total mass subtracted from the initial total mass and divided to the time of treatment. Stone ablation rates were measured according to different laser settings with total power of 10W (0,5J-20 Hz, 1J-10 Hz, 2J-5 Hz) and 60W (1J-60 Hz, 1,5J-40 Hz, 2J-30 Hz). Results: Higher pulse rates and higher total power settings were related to higher ablation rates. Short pulse duration was more effective on soft stones, whereas long pulse duration was more effective on hard stones. For the same power settings, the highest energy-lowest frequency combination resulted in higher ablation rate in comparison to the lowest energy-higher frequency combination. Finally, short and long pulse average ablation rates do not differ so much. Conclusion: Regardless of the stone type and pulse duration, utilization of higher power settings with higher energies increased the ablation rates. Higher ablation rates were demonstrated for hard stones using long pulse duration, and for soft stones with short pulse duration.

Perceptually Optimal Color Representation of Fully Polarimetric SAR Imagery.

The four bands of fully polarimetric SAR data convey scattering characteristics of the Earth's background, but perceptually are not very easy for an observer to use. In this work, the four different channels of fully polarimetric SAR images, namely HH, HV, VH, and VV, are combined so that a color image of the Earth's background is derived that is perceptually excellent for the human eye and at the same time provides accurate information regarding the scattering mechanisms in each pixel. Most of the elementary scattering mechanisms are related to specific color and land cover types. The innovative nature of the proposed approach is due to the two different consecutive coloring procedures. The first one is a fusion procedure that moves all the information contained in the four polarimetric channels into three derived RGB bands. This is achieved by means of Cholesky decomposition and brings to the RGB output the correlation properties of a natural color image. The second procedure moves the color information of the RGB image to the CIELab color space, which is perceptually uniform. The color information is then evenly distributed by means of color equalization in the CIELab color space. After that, the inverse procedure to obtain the final RGB image is performed. These two procedures bring the PolSAR information regarding the scattering mechanisms on the Earth's surface onto a meaningful color image, the appearance of which is close to Google Earth maps. Simultaneously, they give better color correspondence to various land cover types compared with existing SAR color representation methods.

Stone ablation rates using innovative pulse modulation technology: Vapor tunnel, virtual basket, and bubble blast. An in vitro experimental study.

INTRODUCTION AND OBJECTIVES: Virtual BasketTM , Bubble BlastTM , and Vapor TunnelTM are three laser pulse modulation technologies that modify the holmium: yttrium-aluminum-garnet (Ho:YAG) laser pulse transmission through the creation of bubbles emerging from the fiber tip with different effects on the target stone. The primary outcome of the current study was to test the stone ablation rates for the different pulse modulation modes, Virtual Basket, Bubble Blast, and Vapor Tunnel, using different power, energy, and frequency settings. MATERIALS AND METHODS: Quanta Cyber: Ho 150 WTM , a 365 µm PrecisionTM fiber, and hard and soft phantom BegoStonesTM were used in an in vitro experimental configuration in a saline bath. In the Virtual Basket mode, the combinations of power, energy and frequency were tested; 10 W = 0.5 J × 20 Hz, 10 W = 0.5 J × 20 Hz, 60 W = 1 J × 60 Hz and 60 W = 2 J × 30 Hz. In the Bubble Blast mode, the combinations, 12 W = 1.2J × 10 Hz, 60 W = 1.2J × 50 Hz and 60 W = 2 J × 30 Hz, were tested. Similarly, the combination of 10 W = 0.5 J × 20 Hz was tested with Vapor Tunnel mode. High-speed camera captures of the bubble formation and regular photographs of the fragmentation pattern were also taken for each mode. RESULTS: High power lithotripsy was faster and related to higher ablation rates. The Virtual Basket, Bubble Blast, and Vapor Tunnel modalities showed different ablation rates for the same energy and frequency settings. For hard stones, there was an improvement in the ablation rate using 60 W = 2 J × 30 Hz compared with 60 W = 1 J × 60 Hz and 60 W = 1.2 J × 50 Hz. The highest ablation rates were recorded using the Virtual Basket mode with the high-power settings of 2 J of energy and 30 Hz of frequency. CONCLUSIONS: The Virtual BasketTM pulse-modulation technology was related to the highest ablation rates for both hard and soft stones, compared to the Bubble BlastTM and the Vapor TunnelTM technologies in high-power and low-power lithotripsy respectively. For the same high power settings, higher energy seems to provide higher ablation rates.

Stone Retropulsion with Ho: YAG and Tm: YAG Lasers: A Clinical Practice-Oriented Experimental Study.

OBJECTIVE: To compare the retropulsion of stones with the use of holmium: yttrium aluminum garnet (Ho: YAG) laser and thulium: yttrium aluminum garnet (Tm: YAG) laser in settings that could be used in clinical practice. METHODS: The experimental configuration included a glass tube set in a water bath filled with physiologic saline. Plaster of Paris stones were inserted in the tube. Tm: YAG and Ho: YAG laser systems were used along with a high-speed slow-motion camera. The lasers were activated with different settings. The displacement of the stone was measured according to a custom-made algorithm. RESULTS: Ho: YAG: the retropulsion of stones was the lowest with the energy setting of 0.5 J and the frequency of 20 Hz with long pulse duration. The highest retropulsion was observed in the case of 3 J, 5 Hz, and short pulse. Tm: YAG: the retropulsion of stones was the lowest with the energy setting of 1 J and the frequency of 10 Hz with either long or short pulse duration. Practically, there was no retropulsion at all. The highest retropulsion was observed in the case of 8 J, 5 Hz, and short pulse. CONCLUSION: Ho: YAG laser has a linear increase in stone retropulsion with increased pulse energy. On the other hand, the retropulsion rate was kept to the minimum with Tm: YAG as much as the energy level of 8 J. The activation of lasers with short pulse resulted in further displacement of the stone. Lower frequency with the same power setting seemed to result in further stone retropulsion. Higher power with the same frequency setting resulted in further displacement of the stone.

Drunk Person Screening using Eye Thermal Signatures.

Temperature distribution on the eyes of drunk persons is studied by means of thermal infrared images. The sclera and the iris are of the same temperature for the sober person, while for the intoxicated person, the sclera temperature increases. Consequently, only the thermal images from the drunk persons are necessary for intoxication screening. Forty-one participants drank in a controlled alcohol consumption procedure. Their breath alcohol concentration was above the threshold of 0.2 mg/L of exhaled air, which corresponds to about 0.5 mg of alcohol per cubic centimeter of blood. Histogram modification algorithms were employed to prove that for 36 among the 41 intoxicated persons, the sclera becomes hotter. The Student t-test verified with over 99% confidence the drunk discrimination capabilities of the procedure. The forensic science potential contribution of the method is that face infrared imagery is available to the authorities for supporting intoxication in case of criminal actions.

Neural networks for identifying drunk persons using thermal infrared imagery.

Neural networks were tested on infrared images of faces for discriminating intoxicated persons. The images were acquired during controlled alcohol consumption by forty-one persons. Two different experimental approaches were thoroughly investigated. In the first one, each face was examined, location by location, using each time a different neural network, in order to find out those regions that can be used for discriminating a drunk from a sober person. It was found that it was mainly the face forehead that changed thermal behaviour with alcohol consumption. In the second procedure, a single neural structure was trained on the whole face. The discrimination performance of this neural structure was tested on the same face, as well as on unknown faces. The neural networks presented high discrimination performance even on unknown persons, when trained on the forehead of the sober and the drunk person, respectively. Small neural structures presented better generalisation performance.