Dr. Milad Niroumand-Jadidi Profile

Dr. Milad Niroumand-Jadidi

Postdoctoral Research Associate at Fondazione Bruno Kessler

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Publications (6)

Proceedings Article | 28 October 2022 Presentation + Paper

Extreme gradient boosting machine learning for total suspended matter (TSM) retrieval from Sentinel-2 imagery

Milad Niroumand-Jadidi, Francesca Bovolo

Proceedings Volume 12263, 1226308 (2022) https://doi.org/10.1117/12.2638465

KEYWORDS: Machine learning, Water, Statistical modeling, Data modeling, Systems modeling, Image retrieval, Image processing, Error analysis, Atmospheric modeling, Atmospheric corrections

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Proceedings Article | 7 October 2019 Presentation + Paper

Integration of hydro-climatological model and remote sensing for glacier mass balance estimation

Iwona Podsiadlo, Claudia Paris, Francesca Bovolo, Mattia Callegari, Ludovica De Gregorio, Daniel Günther, Carlo Marin, Thomas Marke, Milad Niroumand-Jadidi, Claudia Notarnicola, Ulrich Strasser, Marc Zebisch, Lorenzo Bruzzone

Proceedings Volume 11155, 1115513 (2019) https://doi.org/10.1117/12.2533232

KEYWORDS: Remote sensing, Data modeling, Numerical simulations, Multispectral imaging, Climatology, Meteorology, Statistical modeling, Statistical analysis

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Proceedings Article | 9 October 2018 Presentation + Paper

A novel approach for bathymetry of shallow rivers based on spectral magnitude and shape predictors using stepwise regression

Milad Niroumand-Jadidi, Francesca Bovolo, Alfonso Vitti, Lorenzo Bruzzone

Proceedings Volume 10789, 107890O (2018) https://doi.org/10.1117/12.2325560

KEYWORDS: Radiative transfer, Remote sensing, Data modeling, Ocean optics, Reflectivity, Statistical modeling, Near infrared, Water, Spatial resolution

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Proceedings Article | 3 October 2017 Paper

Grain size mapping in shallow rivers using spectral information: a lab spectroradiometry perspective

Milad Niroumand-Jadidi, Alfonso Vitti

Proceedings Volume 10422, 104220B (2017) https://doi.org/10.1117/12.2278646

KEYWORDS: Reflectivity, Spectroscopy, Radiative transfer, Remote sensing, Radiometry, Image resolution, Spatial resolution, Water, Signal attenuation

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Every individual attribute of a riverine environment defines the overall spectral signature to be observed by an optical sensor. The spectral characteristic of riverbed is influenced not only by the type but also the roughness of substrates. Motivated by this assumption, potential of optical imagery for mapping grain size of shallow rivers (< 1 m deep) is examined in this research. The previous studies concerned with grain size mapping are all built upon the texture analysis of exposed bed material using very high resolution (i.e. cm resolution) imagery. However, the application of texturebased techniques is limited to very low altitude sensors (e.g. UAVs) to ensure the sufficient spatial resolution. Moreover, these techniques are applicable only in the presence of exposed substrates along the river channel. To address these drawbacks, this study examines the effectiveness of spectral information to make distinction among grain sizes for submerged substrates. Spectroscopic experiments are performed in controlled condition of a hydraulic lab. The spectra are collected over a water flume in a range of water depths and bottoms with several grain sizes. A spectral convolution is performed to match the spectra to WorldView-2 spectral bands. The material type of substrates is considered the same for all the experiments with only variable roughness/size of grains. The spectra observed over dry beds revealed that the brightness/reflectance increases with the grain size across all the spectral bands. Based on this finding, the above-water spectra over a river channel are simulated considering different grain sizes in the bottom. A water column correction method is then used to retrieve the bottom reflectances. Then the inferred bottom reflectances are clustered to segregate among grain sizes. The results indicate high potential of the spectral approach for clustering grain sizes (overall accuracy of 92%) which opens up some horizons for mapping this valuable attribute of rivers using remotely sensed data.

Proceedings Article | 19 October 2016 Presentation + Paper

Improving the accuracies of bathymetric models based on multiple regression for calibration (case study: Sarca River, Italy)

Milad Niroumand-Jadidi, Alfonso Vitti

Proceedings Volume 9999, 99990Q (2016) https://doi.org/10.1117/12.2242083

KEYWORDS: Calibration, Data modeling, Animal model studies, Coastal modeling, Statistical modeling, Water, Radiative transfer, Sensors, Analytical research, Global Positioning System

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Proceedings Article | 14 October 2015 Paper

Sub-pixel mapping of water boundaries using pixel swapping algorithm (case study: Tagliamento River, Italy)

Milad Niroumand-Jadidi, Alfonso Vitti

Proceedings Volume 9638, 96380G (2015) https://doi.org/10.1117/12.2194579

KEYWORDS: Earth observing sensors, Picosecond phenomena, Image resolution, Water, Accuracy assessment, Associative arrays, Image segmentation, Spatial resolution, Image processing, Image processing algorithms and systems

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Showing 5 of 6 publications

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