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Simulation of a simultaneous traceable spectroradiometric calibration of an imaging spectrometer

APPLIED OPTICS 2025年第4期64卷 782-796页

作者： Trim, Simon a. Buffat, Jim Hueni, Andreas Univ Zurich Dept Geog Remote Sensing Labs RSL Winterthurerstr 190 CH-8057 Zurich Switzerland Forschungszentrum Julich Inst IAS 8 Data Analyt & Machine Learning & IBG-2 Wilhelm Johnen Str D-52428 Julich Germany

Spectroradiometric calibration aims to determine the instrumental spectral response function (ISRF) parameters and radiometric coefficients of an instrument's spectral bands across all spatial pixels. Typically, this is done by making separate spectral and radiometric calibration measurements. We present a method for the simultaneous traceable spectroradiometric calibration of an imaging spectrometer, using the Spectroscopically Tunable Absolute Radiometric, calibration and characterisation, Optical Ground Support Equipment (STAR-cc-OGSE) facility. We performed the forward simulation of calibration data acquisition by convolving input spectra with the sensor model's response and simulated a slit scattering function (SSF)-based calibration, allowing for both ISRF coefficients and the absolute spectral responsivities to be accurately retrieved from a single series of measurements. We show how the SSF method minimizes uncertainties compared to the traditional spectroradiometric calibration approach. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Calibration Laser systems Point spread function Remote sensing Single pixel imaging Spectral discrimination

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Self-Supervised learning based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation

arXiv

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arXiv 2025年

作者： Wang, Qin Bruns, Benjamin Scharr, Hanno Krajsek, Kai IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Germany Forschungszentrum Jülich Germany

The equivariant behaviour of features is essential in many computer vision tasks, yet popular self-supervised learning (SSL) methods tend to constrain equivariance by design. We propose a self-supervised learning approach where the system learns transformations independently by reconstructing images that have undergone previously unseen transformations. Specifically, the model is tasked to reconstruct intermediate transformed images, e.g. translated or rotated images, without prior knowledge of these transformations. This auxiliary task encourages the model to develop equivariance-coherent features without relying on predefined transformation rules. To this end, we apply transformations to the input image, generating an image pair, and then split the extracted features into two sets per image. One set is used with a usual SSL loss encouraging invariance, the other with our loss based on the auxiliary task to reconstruct the intermediate transformed images. Our loss and the SSL loss are linearly combined with weighted terms. Evaluating on synthetic tasks with natural images, our proposed method strongly outperforms all competitors, regardless of whether they are designed to learn equivariance. Furthermore, when trained alongside augmentation-based methods as the invariance tasks, such as iBOT or DINOv2, we successfully learn a balanced combination of invariant and equivariant features. Our approach performs strong on a rich set of realistic computer vision downstream tasks, almost always improving over all baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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A multi-layer perceptron approach for SIF retrieval in the O2-A absorption band from hyperspectral imagery of the HyPlant airborne sensor system

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REMOTE SENSING OF ENVIRONMENT 2025年 318卷

作者： Buffat, Jim Pato, Miguel Alonso, Kevin Auer, Stefan Carmona, Emiliano Maier, Stefan Mueller, Rupert Rademske, Patrick Siegmann, Bastian Rascher, Uwe Scharr, Hanno Forschungszentrum Julich Inst Bio & Geosci IBG 2 Plant Sci Julich Germany German Aerosp Ctr DLR Remote Sensing Technol Inst Oberpfaffenhofen Germany European Space Agcy ESA Star Grp I-00044 Frascati Italy Forschungszentrum Julich Inst Adv Simulat IAS 8 Data Analyt & Machine Learning Julich Germany

Accurate estimation of solar-induced fluorescence (SIF) from passively sensed hyperspectral remote sensing data has been identified as fundamental in assessing the photosynthetic activity of plants for various scientific and ecological applications at different spatial scales. Different techniques to derive SIF have been developed over the last decades. In view of ESA's upcoming Earth Explorer satellite mission FLEX aiming to provide high- quality global imagery for SIF retrieval an increased interest is placed in physical approaches. We present a novel method to retrieve SIF in the O2-A absorption band of hyperspectral imagery acquired by the HyPlant sensor system. It aims at a tight integration of physical radiative transfer principles and self-supervised neural network training. To this end, a set of spatial and spectral constraints and a specific loss formulation are adopted. Ina validation study we find good agreement between our approach and established retrieval methods as well as with in-situ top-of-canopy SIF measurements. In two application studies, we additionally find evidence that the estimated SIF (i) satisfies a first-order model of diurnal SIF variation and (ii) locally adapts the estimated optical depth in topographically variable terrain.

关键词： Solar-induced fluorescence Spectral fitting methods Deep learning Neural networks Hyperspectral sensors Remote sensing SIF retrieval

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Predicting hemodynamic parameters based on arterial blood pressure waveform using self-supervised learning and fine-tuning

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APPLIED INTELLIGENCE 2025年第6期55卷 1-26页

作者： Liao, Ke Elibol, Armagan Gao, Ziyan Meng, Lingzhong Chong, Nak Young Japan Adv Inst Sci & Technol Sch Informat Sci 1-1 Asahidai Nomi Ishikawa 9231292 Japan Forschungszentrum Julich GmbH Inst Adv Simulat IAS 8 Data Analyt & Machine Learning Wilhelm Johnen Str D-52428 Julich Germany Indiana Univ Sch Med Dept Anesthesia 340 West 10th StFairbanks HallSuite 6200 Indianapolis IN 46202 USA

The arterial blood pressure waveform (ABPW) serves as a less invasive technique for evaluating hemodynamic parameters, offering a lower risk compared to the more invasive pulmonary artery catheter (PAC) thermodilution method. Various studies suggest that deep learning models can potentially predict the hemodynamic parameters of ABPW. However, the scarcity of ground truth data restricts the accuracy of these models, preventing them from gaining clinical acceptance. To mitigate this data and domain challenge, this work proposed a self-supervised generative learning model for hemodynamic parameter prediction, called SSHemo (Self-Supervised Hemodynamic model). Specifically, SSHemo suggests first to leverage large amounts of unlabeled ABPW data to learn the representative embedding and then to fine-tune for the downstream task with a small amount of hemodynamic parameters' ground truth. To verify the effectiveness of SSHemo, we utilize the public available VitalDB data set to train the model, and evaluation was conducted on two public datasets: VitalDB and MIMIC. The experimental results reveal that SSHemo's regression mean absolute error (MAE) improved significantly from 1.63 L/min to 1.25 L/min when predicting cardiac output (CO). The trending tracking ability for CO changes meets clinical acceptance (radial limit of agreement (LOA) is +/- 25.56\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm 25.56$$\end{document}degrees, less than +/- 30\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm 30$$\end{document}degrees). In addition, SSHemo demonstrates robust stability in various conditions and cohorts, as eviden

关键词： Hemodynamic prediction ABPW Self-supervised learning

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DISCO: Internal Evaluation of Density-Based Clustering

arXiv

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arXiv 2025年

作者： Beer, Anna Krieger, Lena Weber, Pascal Ritzert, Martin Assent, Ira Plant, Claudia Faculty of Computer Science University of Vienna Vienna Austria IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Jülich Germany UniVie Doctoral School Computer Science University of Vienna Vienna Austria Data Science @ Uni Vienna University of Vienna Vienna Austria Institute of Computer Science and Campus Institute Data Science University of Göttingen Göttingen Germany Department of Computer Science Aarhus University Aarhus Denmark

In density-based clustering, clusters are areas of high object density separated by lower object density areas. This notion supports arbitrarily shaped clusters and automatic detection of noise points that do not belong to any cluster. However, it is challenging to adequately evaluate the quality of density-based clustering results. Even though some existing cluster validity indices (CVIs) target arbitrarily shaped clusters, none of them captures the quality of the labeled noise. In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels. DISCO reliably evaluates density-based clusters of arbitrary shape by assessing compactness and separation. It also introduces a direct assessment of noise labels for any given clustering. Our experiments show that DISCO evaluates density-based clusterings more consistently than its competitors. It is additionally the first method to evaluate the complete labeling of density-based clustering methods, including noise labels. © 2025, CC BY.

关键词： Cluster analysis

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Adapting Agricultural Virtual Environments in Game Engines to Improve HPC Accessibility 6th

Adapting Agricultural Virtual Environments in Game Engines ...

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6th Nordic e-Infrastructure Collaboration Conference, NeIC 2024

作者： Baker, Dirk Norbert Bauer, Felix Schnepf, Andrea Scharr, Hanno Riedel, Morris Göbbert, Jens Henrik Hvannberg, Ebba School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich Germany Institute of Bio- and Geosciences 3: Agrosphere Forschungszentrum Jülich GmbH Jülich Germany Institute for Advanced Simulation 8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany

ISBN: (纸本)9783031862397

E-infrastructures deliver basic supercomputing and storage capabilities but can benefit from innovative higher-level services that enable use-cases in critical domains, such as environmental and agricultural science. This work describes methods to distribute virtual scenes to the GPU nodes of a modular supercomputer for data generation. High information density virtual scenes, containing >100k geometries, typically cannot be rendered in real-time without techniques that change the information content, such as level-of-detail or culling approaches. Our work enables the concurrent and partitioned coupling to the image analysis in such a way that the data generation is dynamic and can be allocated to GPU nodes on demand, resulting in the possibility of moving through a continuous virtual scene rendered on multiple nodes. Within agricultural data analysis, the approach is especially impactful as virtual fields contain many individual geometries that coexist in one continuous system. Our work facilitates the generation of high-quality image data sets, which has the potential to solve the challenge of scarcity of well-annotated data in agricultural science. We use real-time communication standards to couple the data production with the image analysis training. We demonstrate how the use-case rendering impacts effective use of the compute nodes and furthermore develop techniques to distribute the workload to improve the data production. © The Author(s) 2025.

关键词： Virtual environments

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VRoot: A VR-Based application for manual root system architecture reconstruction

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Plant Phenomics 2025年第2期7卷

作者： Baker, Dirk N. Selzner, Tobias Göbbert, Jens Henrik Scharr, Hanno Riedel, Morris Hvannberg, Ebba þóra Schnepf, Andrea Zielasko, Daniel Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich Germany School of Engineering and Natural Sciences University of Iceland Reykjavík Iceland Institute of Bio- and Geosciences 3: Agrosphere Forschungszentrum Jülich GmbH Jülich Germany Institute of Advanced Simulation 8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany Human-Computer Interaction Trier University Trier Germany

This article describes an immersive virtual reality reconstruction tool for root system architectures from 3D scans of soil columns. In practical scenarios, experimental conditions will be adapted to fit the need of the data analysis pipeline, including sieving and drying the soil before scanning. Based on previous reports of automatic systems that do not represent what experts would annotate, we developed a virtual reality system to assist with the extraction of root systems in cases in which automated approaches fall short of expert knowledge. The aim of the present study is to evaluate whether our immersive method is superior to classical annotation approaches when tested on synthetic data sets using untrained participants. Our laboratory user study consists of evaluating the root extractions of participants, along with their rating on central user experience and usability measures. We show significant improvement in F1 score across conditions (noisy or clear data) as well as an improved usability. Our study highlights that using virtual reality in root extraction improves accuracy, and we perform an in-depth evaluation of biases that occur when users trace roots in soil volumes. © 2025

关键词： Photointerpretation

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On the Generality and Application of Mason's Voting Theorem to Center of Mass Estimation for Pure Translational Motion

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 2656-2671页

作者： Gao, Ziyan Elibol, Armagan Chong, Nak Young Japan Adv Inst Sci & Technol Sch Informat Sci Nomi 9231292 Japan Forschungszentrum Julich Inst Adv Simulat Data Analyt & Machine Learning IAS 8 D-52428 Julich Germany

Object rearrangement is widely demanded in many of the manipulation tasks performed by industrial and service robots. Rearranging an object through planar pushing is deemed energy efficient and safer compared with the pick-and-place operation. However, due to the unknown physical properties of the object, rearranging an object toward the target position is difficult to accomplish. Even though robots can benefit from multimodal sensory data for estimating novel object dynamics, the exact estimation error bound is still unknown. In this work, first, we demonstrate a way to obtain an error bound on the center of mass (CoM) estimation for the novel object only using a position-controlled robot arm and a vision sensor. Specifically, we extend Mason's Voting Theorem to object CoM estimation in the absence of accurate information on friction and object shape. The probable CoM locations are monotonously narrowed down to a convex region, and the extended voting theorems' guarantee that the convex region contains the CoM ground truth in the presence of contact normal estimation error and pushing execution error. For the object translation task, existing methods generally assume that the pusher-object system's physical properties and full-state feedback are available, or utilize iterative pushing executions, which limits the application of planar pushing to real-world settings. In this work, assuming a nominal friction coefficient between the pusher and object through contact normal error bound analysis, we leverage the estimated convex region and the Zero Moment Two Edge Pushing method (Gao et al., 2023) to select the contact configurations for object pure translation. It is ensured that the selected contact configurations are capable of tolerating the CoM estimation error. The experimental results show that the object can be accurately translated to the target position with only two controlled pushes at most.

关键词： Friction Shape Robots Estimation error Force measurement Ellipsoids Visualization Nonprehensile manipulation object physical property estimation planar pushing

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Equivariant Representation learning for Augmentation-based Self-Supervised learning via Image Reconstruction

arXiv

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arXiv 2024年

作者： Wang, Qin Krajsek, Kai Scharr, Hanno IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Germany Forschungszentrum Jülich Germany

Augmentation-based self-supervised learning methods have shown remarkable success in self-supervised visual representation learning, excelling in learning invariant features but often neglecting equivariant ones. This limitation reduces the generalizability of foundation models, particularly for downstream tasks requiring equivariance. We propose integrating an image reconstruction task as an auxiliary component in augmentation-based self-supervised learning algorithms to facilitate equivariant feature learning without additional parameters. Our method implements a cross-attention mechanism to blend features learned from two augmented views, subsequently reconstructing one of them. This approach is adaptable to various datasets and augmented-pair based learning methods. We evaluate its effectiveness on learning equivariant features through multiple linear regression tasks and downstream applications on both artificial (3DIEBench) and natural (ImageNet) datasets. Results consistently demonstrate significant improvements over standard augmentation-based self-supervised learning methods and state-of-the-art approaches, particularly excelling in scenarios involving combined augmentations. Our method enhances the learning of both invariant and equivariant features, leading to more robust and generalizable visual representations for computer vision tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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ROBUST APPROXIMATE CHARACTERIZATION OF SINGLE-CELL HETEROGENEITY IN MICROBIAL GROWTH 21

ROBUST APPROXIMATE CHARACTERIZATION OF SINGLE-CELL HETEROGEN...

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21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Paul, Richard D. Seiffarth, Johannes Scharr, Hanno Noeh, Katharina Forschungszentrum Julich IAS Data Analyt & Machine Learning 8 Julich Germany Forschungszentrum Julich IBG Biotechnol 1 Julich Germany Rhein Westfal TH Aachen Computat Syst Biotechnol AVT CSB Aachen Germany

ISBN: (纸本)9798350313345;9798350313338

Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results.

关键词： Cell cycle times stochastic simulation live-cell microscopy single-cell analysis Bayesian inference

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