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Oxygen bubble accumulation on the anodic side of a polymer exchange membrane water electrolyzer (PEMWE) may cause a decrease in performance. To understand the behavior of these bubbles, a deep-learning-based bubble flow recognition tool dedicated to a PEMWE is developed. Combining the transparent side of a single PEMWE cell with a high-resolution high-speed camera allows us to acquire images of the two-phase flow in the channels. From these images, a deep learning vision system using a fine-tuned YOLO V7 model is applied to detect oxygen bubbles. The tool achieved a high mean average precision of 70%, confirmed the main observations in the literature, and provided exciting insights into the characteristics of two-phase flow regimes. In fact, increasing the water flow rate from 0.05 to 0.4 L/min decreases the bubble coverage (by around 32%) and the mean single-bubble area. In addition, increasing the current density from 0.3 to 1.4 A/cm2 leads to an increase in bubble coverage (by around 40%) and bubble amount.

Monitoring crops and weeds is a major challenge in agriculture and food production today. Weeds compete directly with crops for moisture, nutrients, and sunlight. They therefore have a significant negative impact on crop yield if not sufficiently controlled. Weed detection and mapping is an essential step in weed control. Many existing research studies recognize the importance of remote sensing systems and machine learning algorithms in weed management. Deep learning approaches have shown good performance in many agriculture-related remote sensing tasks, such as plant classification, disease detection, etc. However, despite the success of these approaches, they still face many challenges such as high computation cost, the need of large labelled datasets, intra-class discrimination (in growing phase weeds and crops share many attributes similarity as color, texture, and shape), etc. This paper aims to show that the attention-based deep network is a promising approach to address the forementioned problems, in the context of weeds and crops recognition with drone system. The specific objective of this study was to investigate visual transformers (ViT) and apply them to plant classification in Unmanned Aerial Vehicles (UAV) images. Data were collected using a high-resolution camera mounted on a UAV, which was deployed in beet, parsley and spinach fields. The acquired data were augmented to build larger dataset, since ViT requires large sample sets for better performance, we also adopted the transfer learning strategy. Experiments were set out to assess the effect of training and validation dataset size, as well as the effect of increasing the test set while reducing the training set. The results show that with a small labeled training dataset, the ViT models outperform state-of-the-art models such as EfficientNet and ResNet. The results of this study are promising and show the potential of ViT to be applied to a wide range of remote sensing image analysis tasks.

Imaging large fields of view while preserving high-resolution structural information remains a challenge in low-dose cryo-electron tomography. Here we present robust tools for montage parallel array cryo-tomography (MPACT) tailored for vitrified specimens. The combination of correlative cryo-fluorescence microscopy, focused-ion-beam milling, substrate micropatterning, and MPACT supports studies that contextually define the three-dimensional architecture of cells. To further extend the flexibility of MPACT, tilt series may be processed in their entirety or as individual tiles suitable for sub-tomogram averaging, enabling efficient data processing and analysis. Montage parallel array cryo-tomography adopts principles of montage tomography via regular array beam-image-shift montage acquisition and is robust for imaging large fields of view while retaining high-resolution structural information in cryo-electron tomography.

High-resolution optical cameras have always been important scientific payloads in Mars exploration missions, which can obtain detailed images of Martian surface for the study of geomorphology, topography and geological structure. At present, there are still many challenges for Mars high-resolution images in terms of global coverage, stereo coverage (especially for colour images), and data processing methods. High Resolution Imaging Camera (HiRIC) is a high-quality, multi-mode, multi-functional, multi-spectral remote sensing camera that is suitable for the deep space developed for China’s first Mars Exploration Mission (Tianwen-1), which was successfully launched in July 2020. Here we design special experiments based on the in-orbit detection conditions of Tianwen-1 mission to comprehensively verify the detection capability and the performance of HiRIC, from the aspects of image motion compensation effect, focusing effect, image compression quality, and data preprocessing accuracy. The results showed that the performance status of HiRIC meets the requirements of obtaining high resolution images on the Martian surface. Furthermore, proposals for HiRIC in-orbit imaging strategy and data processing are discussed to ensure the acquisition of high-quality HiRIC images, which is expected to serve as a powerful complementation to the current Mars high-resolution images.

The selection of the Discovery Program InSight landing site took over four years from initial identification of possible areas that met engineering constraints, to downselection via targeted data from orbiters (especially Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) and High-Resolution Imaging Science Experiment (HiRISE) images), to selection and certification via sophisticated entry, descent and landing (EDL) simulations. Constraints on elevation ( ≤ − 2.5 km for sufficient atmosphere to slow the lander), latitude (initially 15°S–5°N and later 3°N–5°N for solar power and thermal management of the spacecraft), ellipse size (130 km by 27 km from ballistic entry and descent), and a load bearing surface without thick deposits of dust, severely limited acceptable areas to western Elysium Planitia. Within this area, 16 prospective ellipses were identified, which lie ∼600 km north of the Mars Science Laboratory (MSL) rover. Mapping of terrains in rapidly acquired CTX images identified especially benign smooth terrain and led to the downselection to four northern ellipses. Acquisition of nearly continuous HiRISE, additional Thermal Emission Imaging System (THEMIS), and High Resolution Stereo Camera (HRSC) images, along with radar data confirmed that ellipse E9 met all landing site constraints: with slopes <15° at 84 m and 2 m length scales for radar tracking and touchdown stability, low rock abundance (<10 %) to avoid impact and spacecraft tip over, instrument deployment constraints, which included identical slope and rock abundance constraints, a radar reflective and load bearing surface, and a fragmented regolith ∼5 m thick for full penetration of the heat flow probe. Unlike other Mars landers, science objectives did not directly influence landing site selection.

ObjectivesTo compare the image quality of the lungs between ultra-high-resolution CT (U-HRCT) and conventional area detector CT (AD-CT) images.MethodsImage data of slit phantoms (0.35, 0.30, and 0.15 mm) and 11 cadaveric human lungs were acquired by both U-HRCT and AD-CT devices. U-HRCT images were obtained with three acquisition modes: normal mode (U-HRCTN: 896 channels, 0.5 mm × 80 rows; 512 matrix), super-high-resolution mode (U-HRCTSHR: 1792 channels, 0.25 mm × 160 rows; 1024 matrix), and volume mode (U-HRCTSHR-VOL: non-helical acquisition with U-HRCTSHR). AD-CT images were obtained with the same conditions as U-HRCTN. Three independent observers scored normal anatomical structures (vessels and bronchi), abnormal CT findings (faint nodules, solid nodules, ground-glass opacity, consolidation, emphysema, interlobular septal thickening, intralobular reticular opacities, bronchovascular bundle thickening, bronchiectasis, and honeycombing), noise, artifacts, and overall image quality on a 3-point scale (1 = worst, 2 = equal, 3 = best) compared with U-HRCTN. Noise values were calculated quantitatively.ResultsU-HRCT could depict a 0.15-mm slit. Both U-HRCTSHR and U-HRCTSHR-VOL significantly improved visualization of normal anatomical structures and abnormal CT findings, except for intralobular reticular opacities and reduced artifacts, compared with AD-CT (p < 0.014). Visually, U-HRCTSHR-VOL has less noise than U-HRCTSHR and AD-CT (p < 0.00001). Quantitative noise values were significantly higher in the following order: U-HRCTSHR (mean, 30.41), U-HRCTSHR-VOL (26.84), AD-CT (16.03), and U-HRCTN (15.14) (p < 0.0001). U-HRCTSHR and U-HRCTSHR-VOL resulted in significantly higher overall image quality than AD-CT and were almost equal to U-HRCTN (p < 0.0001).ConclusionsBoth U-HRCTSHR and U-HRCTSHR-VOL can provide higher image quality than AD-CT, while U-HRCTSHR-VOL was less noisy than U-HRCTSHR.Key Points• Ultra-high-resolution CT (U-HRCT) can improve spatial resolution.• U-HRCT can reduce streak and dark band artifacts.• U-HRCT can provide higher image quality than conventional area detector CT.• In U-HRCT, the volume mode is less noisy than the super-high-resolution mode.• U-HRCT may provide more detailed information about the lung anatomy and pathology.

CORONA spy satellites offer high spatial resolution imagery acquired in the 1960s and early 1970s and declassified in 1995, and they have been used in various scientific fields, such as archaeology, geomorphology, geology, and land change research. The images are panchromatic but contain many details of objects on the land surface due to their high spatial resolution. This systematic review aims to study the use of CORONA imagery in land use and land cover change (LULC) research. Based on a set of queries conducted on the SCOPUS database, we identified and examined 54 research papers using such data in their study of LULC. Our analysis considered case-study area distributions, LULC classes and LULC changes, as well as the methods and types of geospatial data used alongside CORONA data. While the use of CORONA images has increased over time, their potential has not been fully explored due to difficulties in processing CORONA images. In most cases, study areas are small and below 5000 km2 because of the reported drawbacks related to data acquisition frequency, data quality and analysis. While CORONA imagery allows analyzing built-up areas, infrastructure and individual buildings due to its high spatial resolution and initial mission design, in LULC studies, researchers use the data mostly to study forests. In most case studies, CORONA imagery was used to extend the study period into the 1960s, with only some examples of using CORONA alongside older historical data. Our analysis proves that in order to detect LULC changes, CORONA can be compared with various contemporary geospatial data, particularly high and very high-resolution satellite imagery, as well as aerial imagery.

High-resolution Digital Surface Models (DSMs) are crucial for diverse geospatial analyses and UAS photogrammetry offers a cost-effective option for DSM acquisition. However, corridor mapping, due to its linear geometry, challenges the extraction of 3D information without relying on Ground Control Points (GCPs). While onboard GNSS-RTK can improve accuracy, robust camera calibration is critical to mitigate systematic vertical errors propagating in derived DSM. Existing research lacks sufficient investigation into feasible pre-calibration strategies for corridor mapping without GCPs. Therefore, this study addresses this gap by evaluating the precision of DSMs obtained from five photogrammetric experiments without GCPs: one on-the-job calibration and four GNSS-Assisted Aerial Triangulation using on-site pre-calibrations with different sub-blocks of images. For precision assessment of DSMs, a reference experiment with 17 GCPs and all available images was also carried out. Our results show that including oblique images in on-site pre-calibration with sub-blocks significantly reduced the critical correlation between focal length and Z object-space coordinates (from 99% to less than 20%). That outcome directly influenced focal length estimation and allowed mitigation of vertical bias in generated DSMs. The results demonstrate that on-site pre-calibration notably improved the accuracy and precision of vertical spatial data acquisition. These findings highlight on-site oblique pre-calibration with a sub-block of images as a feasible and robust strategy for producing high-resolution 3D models in UAS corridor mapping, significantly reducing reliance on GCPs.

The wood industry is facing many challenges. The high variability of raw material and the complexity of manufacturing processes results in a wide range of visible structure defects, which have to be controlled by trained specialists. These manual processes are not only tedious and biased, but also less effective. To overcome the drawbacks of the manual quality control processes, several automated vision-based systems have been proposed. Even though some conducted studies achieved a higher recognition rate than trained experts, researchers have to deal with a lack of large-scale databases and authentic data in this field. To address this issue, we performed a data acquisition experiment set in the industrial environment, where we were able to acquire an extensive set of authentic data from a production line. For this purpose, we designed and implemented a complex technical solution suitable for high-speed acquisition during harsh manufacturing conditions. In this data note, we present a large-scale dataset of high-resolution sawn timber surface images containing more than 43 000 labelled surface defects and covering 10 types of the most common wood defects. Moreover, with each image record, we provide two types of labels allowing researchers to perform semantic segmentation, as well as defect classification, and localization.

Significance: Hyperspectral imaging (HSI) provides rich spectral information for improved histopathological cancer detection. However, acquiring high-resolution HSI data for whole-slide imaging (WSI) can be time-consuming and requires a huge amount of storage space. Aim: WSI using a color camera can be achieved with fast speed, high image resolution, and excellent image quality due to the established techniques. We aim to develop an RGB-guided unsupervised hyperspectral super-resolution reconstruction method that is hypothesized to improve image quality while maintaining the spectral characteristics. Approach: High-resolution hyperspectral images of 32 histologic slides were obtained via automated WSI. High-resolution RGB histology images were registered to the hyperspectral images for RGB guidance. An unsupervised super-resolution network was trained to take the downsampled low-resolution hyperspectral patches (LR-HSI) and high-resolution RGB patches (HR-RGB) as inputs to reconstruct high-resolution hyperspectral patches (HR-HSI). Then, an Inception-based network was trained with the HR-RGB, original HR-HSI, and generated HR-HSI, respectively, for whole-slide histopathological cancer detection. Results: Our super-resolution reconstruction network generated high-resolution hyperspectral images with well-maintained spectral characteristics and improved image quality. Image classification using the original hyperspectral data outperformed RGB because of the extra spectral information. The generated hyperspectral image patches further improved the results. Conclusions: The proposed method potentially reduces image acquisition time, saves storage space without compromising image quality, and improves the image classification performance.