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1. Airborne Validation of ICESat-2 ATLAS Data over Crevassed Surfaces and Other Complex Glacial Environments: Results from Experiments of Laser Altimeter and Kinematic GPS Data Collection from a Helicopter over a Surging Arctic Glacier (Negribreen, Svalbard)

   https://doi.org/10.3390/rs14051185  

   Herzfeld, Ute C.; Lawson, Matthew; Trantow, Thomas; Nylen, Thomas (March 2022, Remote Sensing)

   The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7 m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905 nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1) Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2) Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3) Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25 m, while distance between predicted and actual ICESat-2 ground track was on the order of 9 m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4) The Lasertech Universal Laser System (ULS), operated at up to 300 m above ground level, yields full return frequency (400 Hz) and 0.06–0.08 m on-ice along-track spacing of height measurements. (5) Cross-over differences of airborne laser altimeter data are −0.172 ± 2.564 m along straight paths, which implies a precision of approximately 2.6 m for ICESat-2 validation experiments in crevassed terrain. (6) In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)–(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. Numerical evaluation of height comparisons utilizes spatial surface roughness measures. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge. 

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2. Decoding the Hidden: Direct Image Classification Using Coded Aperture Imaging

   https://doi.org/10.1109/CAMSAP58249.2023.10403419  

   Munoz, Jocelyn Ornelas; Rutter, Erica M.; Marcia, Roummel F. (December 2023, 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP))

   Coded aperture imaging has emerged as a solution to enhance light sensitivity and enable imaging in challenging conditions. However, the computational expense of image reconstruction poses limitations in processing efficiency. To address this, we propose a direct classification method using convolutional neural networks. By leveraging raw coded measurements, our approach eliminates the need for explicit image reconstruction, reducing computational overhead. We evaluate the effectiveness of this approach compared to traditional methods on the MNIST and CIFAR10 datasets. Our results demonstrate that direct image classification using raw coded measurements achieves comparable performance to traditional methods while reducing computational overhead and enabling real-time processing. These findings highlight the potential of machine learning in enhancing the decoding process and improving the overall performance of coded aperture imaging systems. 

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3. A Data‐Centric Approach to Quantifying the Forward and Inverse Relationship Between Laser Powder Bed Fusion Process Parameters and as‐Built Surface Roughness of IN718 Parts

   https://doi.org/10.1002/aisy.202500409  

   Mahmood, Samsul; Raeymaekers, Bart (September 2025, Advanced Intelligent Systems)

   Laser powder bed fusion (PBF‐LB) is an additive manufacturing (AM) technology for producing complex geometry parts. However, the high cost of post‐processing coarse as‐built surfaces drives the need to control surface roughness during fabrication. Prior studies have evaluated the relationship between process parameters and as‐built surface roughness, but they rely on forward models using trial‐and‐error, regression, and data‐driven methods based only on areal surface roughness parameters that neglect spatial surface characteristics. In contrast, this study introduces, for the first time, an inverse data‐centric framework that leverages machine learning algorithms and an experimental dataset of Inconel 718 as‐built surfaces to predict the PBF‐LB process parameters required to achieve a desired as‐built roughness. This inverse model shows a prediction accuracy of ≈80%, compared to 90% for the corresponding forward model. Additionally, it incorporates deterministic surface roughness parameters, which capture both height and spatial information, and significantly improves prediction accuracy compared to only using areal parameters. The inverse model provides a digital tool to process engineers that enables control of surface roughness by tailoring process parameters. Hence, it establishes a foundation for integrating surface roughness control into the digital thread of AM, thereby reducing the need for post‐processing and improving process efficiency. 

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4. Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

   https://doi.org/10.3390/rs16142679  

   Dorbu, Freda; Hashemi-Beni, Leila (July 2024, Remote Sensing)

   Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level. 

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5. Sensor-aided camera calibration for three dimensional digital image correlation measurements

   https://doi.org/10.1117/12.2657163  

   Bottalico, Fabio; Valente, Nicholas A.; Niezrecki, Christopher; Jerath, Kshitij; Luo, Yan; Sabato, Alessandro (April 2023, Health Monitoring of Structural and Biological Systems XVII)

   Fromme, Paul; Su, Zhongqing (Ed.)

   Stereovision systems can extract full-field three-dimensional (3D) displacements of structures by processing the images collected with two synchronized cameras. To obtain accurate measurements, the cameras must be calibrated to account for lens distortion (i.e., intrinsic parameters) and compute the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking photos of a calibration object (e.g., a checkerboard) with the two cameras. Because the calibration object must be similar in size to the targeted structure, measurements on large-scale structures are highly impractical. This research proposes a multi-sensor board with three inertial measurement units and a laser distance meter to compute the extrinsic parameters of a stereovision system and streamline the calibration procedure. In this paper, the performances of the proposed sensor-based calibration are compared with the accuracy of the traditional image-based calibration procedure. Laboratory experiments show that cameras calibrated with the multi-sensor board measure displacements with 95% accuracy compared to displacements obtained from cameras calibrated with the traditional procedure. The results of this study indicate that the sensor-based approach can increase the applicability of 3D digital image correlation measurements to large-scale structures while reducing the time and complexity of the calibration. 

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