This work presents a practical method for estimating the spatially-varying gain of the signal-dependent portion of the noise from a digital breast tomosynthesis (DBT) system. A number of image processing algorithms require previous knowledge of the noise properties of a DBT unit. However, this information is not easily available and thus must be estimated. The estimation of such parameters requires a large number of calibration images, as it often changes with acquisition angle, spatial position and radiographic factors. This could represent a barrier in the algorithm’s deployment, mainly for clinical applications. Thus, we modeled the gain of the Poisson noise of a commercially available DBT unit as a function of the radiographic factors, acquisition angle, and pixel position. First, we measured the noise parameters of a clinical DBT unit by acquiring 36 sets of calibration images (raw projections) using uniform phantoms of different thicknesses, within a range of radiographic factors commonly used in clinical practice. With this information, we trained a multilayer perceptron artificial neural network (MLP-ANN) to predict the gain of the Poisson noise automatically as a function of the acquisition setup. Furthermore, we varied the number of calibration images in the learning step of the MLP-ANN to determine the minimum number of images necessary to obtain an accurate model. Results show that the MLP-ANN was able to yield the desired parameters with average error of less than 2%, using a learning dataset limited to only seven sets of calibration images. The accuracy of the model, along with its computational efficiency, makes this method an attractive tool for clinical image-based applications.
Large amounts of Earth observation (EO) data have been collected to date, to increase even more rapidly with the upcoming Sentinel data. All this data contains unprecedented information, yet it is hard to retrieve, especially for nonremote sensing specialists. As we live in an urban era, with more than 50% of the world population living in cities, urban studies can especially benefit from the EO data. Information is needed for sustainable development of cities, for the understanding of urban growth patterns or for studying the threats of natural hazards or climate change. Bridging this gap between the technology-driven EO sector and the information needs of environmental science, planning, and policy is the driver behind the TEP-Urban project. Modern information technology functionalities and services are tested and implemented in the Urban Thematic Exploitation Platform (U-TEP). The platform enables interested users to easily exploit and generate thematic information on the status and development of the environment based on EO data and technologies. The beta version of the web platform contains value added basic earth observation data, global thematic data sets, and tools to derive user specific indicators and metrics. The code is open source and the architecture of the platform allows adding of new data sets and tools. These functionalities and concepts support the four basic use scenarios of the U-TEP platform: explore existing thematic content; task individual on-demand analyses; develop, deploy and offer your own content or application; and, learn more about innovative data sets and methods.