Waterleakage detection and localization in distribution networks pipelines is a challenge for utility companies. For this purpose, thermal Infrared Radiation (IR) techniques have been widely applied in the literature. However, the classical analysis of IR images has not been robust in detecting and locating leakage, due to presence of thermal anomalies such as shadows. In this study, to improve the detection and location accuracy, a digital image processing tool based on multitemporal IR is proposed. In multitemporal IR analysis, the variation of soil's temperature due to field temperature can be obtained; and hence; estimating variations due to water leakage would be more accurate. An experimental setup was built to evaluate the proposed multitemporal IR water leak detection method. In order to consider the temporal temperature variation due to water leakage and mitigate the field temperature effects, a luminance transformation of the IRimages was introduced. To determine the temporal temperature variation of the soil's surface due to the leakage, several metrics have been considered such as the difference, the ratio, the log-ratio and the coefficient variation (CV) images. Based on the experimental results, the log-ratio and the CVimages were the most robust metrics. Then, based on log-ratio or the CV image, a temporal variation image (TVI) that traduces the temporal IR luminance variation was introduced. The analysis of the TVI image showed that the CV image is less noisy than the log-ratio image, and can more accurately locate the leakage. Finally, based on TVI histogram, a threshold was defined to classify the TVI image into leakage/non-leakage areas. Results showed that the proposed method is capable of accurately detecting and locating water leakage, which is an improvement to the false detections of spatial thermal IR analysis.

Water leaks in the distribution network produce significant losses and cause serious economic inconvenience especially in areas with water shortage. In this paper, the operational aspects of the most popular offline detection technologies, ground penetrating radars (GPR’s), infrared (IR) cameras, and acoustic detectors, were compared. The authors also studied the potential of using the recent Terahertz imaging technology for the same application. Acoustic detectors were found the most suitable technology for the atmosphere in UAE, where the levels of humidity and, consequently, soil moisture are high, because both of GPRs and IR cameras operational capability to detect leaks tend to decrease sharply as soil moisture increases. On the other side, a conventional acoustic detector has very limited scope of detection. This paper presents a method of expanding the sensing component of acoustic detectors by connecting acoustic sensors through a digital communication system using the 3G/4G networks to a monitoring center with an acoustic spectrum analyzer. The novelty of this system is its ability to provide offline detection of leakages in the underground water pipelines remotely without deforming the surrounding environment or adjusting the acoustic detector’s analyzing system. Simulation results proves the ability of the system to reconstruct the input noise signal at the end of the proposed network which is to be connected to the acoustic analyzer.

The recent advancement in synthetic aperture radar (SAR) technology has enabled high-resolution imaging capability which calls for efficient speckle filtering algorithms to pre-process radar imagery. Since the introduction of Lee sigma filter in 1980, the various versions of the minimum mean square error (MMSE) filter were developed, focusing essentially on how to estimate the processed pixels. For instance, the iterative MMSE (IMMSE) filter which is commonly initialized by the boxcar filter maintains the initially filtered homogeneous areas and corrects the initially blurred spatial details after few iterations. In this study, an effort is made to enhance the performance of the IMMSE filter in terms of speckle reduction and spatial detail preservation by refining the choice of the initial filter, optimizing its parameters and improving the estimation of local statistics. Compared to the basic version, results showed that the improved IMMSE considerably enhanced the filtering criteria. When the IMMSE filtering process was initialized by the Non-Local Mean NLM filter, for few iterations, the IMMSE improved the filtering performances. Simulated, Airborne (ESAR, Oberpfaffenhofen Germany) and Spaceborne (Sentinel 1, Palm Jumeirah Dubai UAE) SAR data were used to assess the filtering performances of the studied filters.

Speckle filtering in synthetic aperture radar (SAR) images is essential for the extraction of significant information for homogeneous extended targets. In this letter, we exploited the minimum mean square error to establish a linear rule between the values of the filtered pixels and their variances. Then, the filtered pixel for infinite number of looks (INL) was predicted by linear regression of means and variances for various window sizes. The results show that the proposed INL-prediction filter improved the filtering performances of the original filters. Simulated and real SAR data were used for validation.

The eigendecompostion of the coherency matrix and the related parameters, i.e., entropy H, α, and anisotropy A, are effective and popular tools for the analysis and quantitative estimation of the physical parameters of polarimetric synthetic aperture radar images. However, the speckle noise constitutes the main obstacle that hinders these goals and should be filtered. In this paper, based on studies obtained from extensive simulated data sets, we tried to determine how this noise is transmitted to sample eigendecomposition parameters. The dependence between means and variances of sample parameters leads to building their speckle models and to the definition of a bias elimination procedure. We found that sample eigenvalues were affected by multiplicative noise, whereas sample entropy and sample anisotropy were affected by a mixture of multiplicative and additive noise sources. In addition to its versatility, independence to knowledge of the equivalent number of looks and high ability to bias compensation, the proposed bias suppression technique reduced the variance of noise. Simulated and real data as well as the existing theories are used for validation in this paper.