In the framework of the Horizon-Europe project “Instantaneous Infrastructure Monitoring by Earth Observation (IIMEO)” the objective is to design, implement and demonstrate key technological factors of a future satellite-based Earth Observation (EO) system capable of providing functions necessary for instantaneous monitoring of infrastructures in near real time. The system will implement a tiled acquisition of multitemporal SAR images over a railway infrastructure and perform near real-time change-obstacle detection at every new acquisition within one hour after the satellite passes over the area. The tile-based obstacle change-detection multitemporal system is explained in detail.
This paper addresses the complex task of detecting and characterizing changes in dense Satellite Image Time Series (SITS). Although Change Vector Analysis (CVA) is widely used for Change Detection (CD), it has limitations due to missing prior information on changes, such as: optimal spectral channels and change timing. Time series data can help overcome these limitations, but working with them is challenging. To address these challenges, the paper introduces a novel framework called Time Series Change Vector Analysis (TSCVA), which builds upon the principles of CVA. In TSCVA, the paper redefines CVA in the time series feature space and introduces new definitions for change in time series magnitude and direction. This allows for a detailed analysis of change components in the time and spectrum domain within the SITS, enabling unsupervised CD. We utilize the expectation-maximization algorithm to estimate parameters of statistical distributions for change and no change classes. The effectiveness of the proposed TSCVA method is evaluated using Sentinel-2 time series data. The results, both quantitative and qualitative, confirm the robustness of this approach in effectively addressing the CD problem in dense SITS.
In this work, we present a study on photonic biosensors based on Si3N4 asymmetric Mach-Zehnder Interferometers (aMZI) for Aflatoxin M1 (AFM1) detection. AFM1 is an hepatotoxic and a carcinogenic toxin present in milk. The biosensor is based on an array of four Si3N4 aMZI that are optimized for 850nm wavelength. We measure the bulk Sensitivity (S) and the Limit of Detection (LOD) of our devices. In the array, three devices are exposed and have very similar sensitivities. The fourth aMZI, which is covered by SiO2, is used as an internal reference for laser (a VCSEL) and temperature fluctuations. We measured a phase sensitivity of 14300±400 rad/RIU. To characterize the LOD of the sensors, we measure the uncertainty of the experimental readout system. From the measurements on three aMZI, we observe the same value of LOD, which is ≈ 4.5×10−7 RIU. After the sensor characterization on homogeneous sensing, we test the surface sensing performances by flowing specific Aflatoxin M1 and non-specific Ochratoxin in 50 mM MES pH 6.6 buffer on the top of the sensors functionalized with Antigen-Recognising Fragments (Fab’). The difference between specific and non-specific signals shows the specificity of our sensors. A moderate regeneration of the sensors is obtained by using glycine solution.
The automatic detection of geometric features, such as edges and creases, from objects represented by 3D point clouds (e.g., LiDAR measurements, Tomographic SAR) is a very important issue in different application domains including urban monitoring and building reconstruction. A limitation of many methods in the literature is that they rely on rasterization or interpolation of the original grid, with consequent potential loss of detail. Recently, a second-order variational model for edge and crease detection and surface regularization has been presented in literature and succesfully applied to DSMs. In this paper we address the generalization of this model to unstructured grids. The model is based on the Blake-Zisserman energy and allows to obtain a regularization of the original data (noise reduction) which does not affect crucial regions containing jumps and creases. Specifically, we focus on the detection of these features by means of two auxiliary functions that are computable by solving specific differential equations. Results obtained on LiDAR data by solving the equations via Finite Element Method are presented.
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