Image Mosaic Research Articles

Dissecting the Crab Nebula with JWST: Pulsar Wind, Dusty Filaments, and Ni/Fe Abundance Constraints on the Explosion Mechanism

We present JWST observations of the Crab Nebula, the iconic remnant of the historical SN 1054. The observations include NIRCam and MIRI imaging mosaics plus MIRI/MRS spectra that probe two select locations within the ejecta filaments. We derive a high-resolution map of dust emission and show that the grains are concentrated in the innermost, high-density filaments. These dense filaments coincide with multiple synchrotron bays around the periphery of the Crab's pulsar wind nebula (PWN). We measure synchrotron spectral index changes in small-scale features within the PWN’s torus region, including the well-known knot and wisp structures. The index variations are consistent with Doppler boosting of emission from particles with a broken power-law distribution, providing the first direct evidence that the curvature in the particle injection spectrum is tied to the acceleration mechanism at the termination shock. We detect multiple nickel and iron lines in the ejecta filaments and use photoionization models to derive nickel-to-iron abundance ratios that are a factor of 3–8 higher than the solar ratio. We also find that the previously reported order-of-magnitude higher Ni/Fe values from optical data are consistent with the lower values from JWST when we reanalyze the optical emission using updated atomic data and account for local extinction from dust. We discuss the implications of our results for understanding the nature of the explosion that produced the Crab Nebula and conclude that the observational properties are most consistent with a low-mass Fe core-collapse supernova, even though an electron-capture explosion cannot be ruled out.

Development of Multi-Strip Image Mosaicking for KOMPSAT-3A Images

Abstract. High-resolution satellite imagery has a limitation in terms of coverage area. This limitation presents challenges for extensive-scale analysis at regional or national levels. To maximize the utility of high-resolution satellite imagery, the implementation of image mosaicking techniques is essential. In this paper, we have developed seamline extraction techniques and relative geometric correction optimized for high-resolution satellite imagery. Ultimately, we proposed a multi-strip image mosaicking method for KOMPSAT-3A (Korea Multi-Purpose Satellite-3A) images. We applied the Dijkstra's shortest path algorithm to efficiently extract seamlines. we also performed image registration based on feature matching and homography transformation to correct the relative geometric errors between input images. We conducted experiments with our methods using 29 scenes from KOMPSAT-3A L1G data. The results indicated high relative geometric accuracy, with an average error of 1.63 pixels. Furthermore, we were able to obtain high-quality seamless mosaic images. Our proposed method is expected to enhance the utility of KOMPSAT-3A imagery for large-scale environmental and urban analysis and to provide more accurate and comprehensive data.

Contractional strains and maximum displacement-length ratios of lunar wrinkle ridges in four Maria of basalt

Compressional stresses from the basalt basins on the lunar are responsible for the formation of wrinkle ridges in lunar mare basalts. According to the wide angle multispectral camera (WAC) mosaic image, we selected 62, 75, 73, and 58 single wrinkle ridges in Mare Imbrium, Mare Serenitatis, Mare Fecunditatis, and Mare Tranquillitatis, respectively, for this paper. Several topographic profiles near the midpoint of each wrinkle ridge are generated to measure the maximum displacement (Dmax) and height of the wrinkle ridges using digital elevation model (LOLA) data. After that, we create 2D plots of displacement-length (L) for the ridge population in the four Maria and compare the results. A linear fit method derives the Dmax/L ratios (γ) from the D-L data. We calculated the contractional strains in each mare area based on the cap D sub m a. x over cap L data. Moreover, each mare's gravity pattern, mare thickness, and formation age are also presented. The ridges in Mare Imbrium and Mare Serenitatis have a higher γ value (1.83 × 10−2 and 1.98 × 10−2) than the ridges in Mare Fecunditatis and Mare Tranquillitatis, which have γ values of (1.67 × 10−2 and 1.75 × 10−2). Finally, the contractional strains (ε) in Mare Imbrium, Mare Serenitatis, Mare Fecunditatis, and Mare Tranquillitatis are estimated to be 0.23 %, 0.41 %, 0.39 %, and 0.18 % (considering 25° is the fault plane dip θ), respectively. The maximum values of the free-air gravity anomalies in Mare Fecunditatis range from −30 to 250 mGal, while minimum gravity anomalies in Mare Serenitatis range from −80 to 140 mGal. Mare Imbrium, Mare Serenitatis, Mare Fecunditatis, and Mare Tranquillitatis have an average thickness of 300 m, 910 m, 652 m, and 760 m, respectively. Furthermore, the Mare Imbrium ridge group is older than the Lunar Wrinkle Ridges in Mare Serenitatis. Mare Tranquillitatis ridge group formation takes longer than Mare Imbrium ridge group formation. Therefore, we believe that it has thicker basaltic units, a longer wrinkle ridge formation time, and higher gravity anomaly than the Mare Imbrium and Mare Serenitatis basins, even though the formation of the Mare Tranquillitatis and Mare Fecunditatis basins occurred earlier.

Increasing the Usability and Accessibility of Voyager 2 Images of Triton

Much of what we know about Neptune’s moon Triton was inferred from the analysis of images returned by the Voyager 2 mission, the only spacecraft to have visited that putative ocean world. Unfortunately, the highest-resolution images (scales < 2 km pixel−1) are difficult to use because they are only available in nonstandard formats, and the locations of the images on Triton’s surface are incorrect by up to 200 km. Although image mosaics of Triton are publicly available, these do not include the highest-resolution data. Here we describe our effort to improve the usability and accessibility of Voyager 2 images of Triton. We used the USGS’s ISIS software to process 41 Triton images, including geometric calibration, radiometric calibration, and reseau removal. We improved the image locations using a photogrammetric control network with 958 points and 3910 image measurements. Least-squares bundle adjustment of the network yielded rms uncertainty of 0.50, 0.52, and 0.51 pixels in latitude, longitude, and radius, respectively, and maximum residuals of −4.21 and +3.20 pixels, respectively. Image-to-image alignment is therefore vastly improved. We have released these processed images as cloud-optimized GeoTIFFs in orthographic projection at the original pixel scale of each image. Associated mosaics have also been created and released to provide geologic context for the individual images. These products provide the science community with analysis-ready data that enable new investigations of Triton, increase accessibility to this unique data set, and continue to enhance the scientific return from the Voyager 2 mission.

A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic

In the mosaic creation of multiple high-resolution synthetic aperture radar (SAR) images, achieving an optimal seamline in overlapping areas is crucial for seamless and visually satisfactory results. Many existing seamline generation methods are designed primarily for optical remote sensing images, but due to the differing characteristics of SAR images and optical images, applying these methods directly to SAR images poses challenges in finding the optimal seamline. In response, this paper proposes a segmentation-based optimal seamline generation (SOSG) method for SAR image mosaics. The SOSG method involves a multi-step process. First, SAR image joint segmentation is performed within the overlapping areas. Subsequently, homogeneous areas are identified based on the segmentation results. Following this, a pixel cost matrix is constructed, incorporating homogeneous areas and intensity differences. Finally, the minimum path cost from the starting pixel to the end pixel is computed using the Dijkstra algorithm to determine the optimal path. To assess the feasibility and effectiveness of the proposed method, experiments are conducted using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets. The experimental results demonstrate that the proposed method yields seamless mosaic images when compared to other methods, while delivering satisfactory outcomes. This indicates the potential of the proposed method in addressing the unique challenges posed by SAR images and enhancing the quality of SAR image mosaics.

GPU Acceleration for SAR Satellite Image Ortho-Rectification

Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified.

Evaluation of Mosaic Image Quality and Analysis of Influencing Factors Based on UAVs

With the growing prominence of UAV-based low-altitude remote sensing in agriculture, the acquisition and processing of high-quality UAV remote sensing images is paramount. The purpose of this study is to investigate the impact of various parameter settings on image quality and optimize these parameters for UAV operations to enhance efficiency and image quality. The study examined the effects of three parameter settings (exposure time, flight altitudes and forward overlap (OF)) on image quality and assessed images obtained under various conditions using signal-to-noise ratio (SNR) and BRISQUE algorithms. The results indicate that the setting of exposure time during UAV image acquisition directly affects image quality, with shorter exposure times resulting in lower SNR. The optimal exposure times for the RGB and MS cameras have been determined as 0.8 ms to 1.1 ms and 4 ms to 16 ms, respectively. Additionally, the best image quality is observed at flight altitudes between 15 and 35 m. The setting of UAV OF complements exposure time and flight altitude; to ensure the completeness of image acquisition, it is suggested that the flight OF is set to approximately 75% at a flight altitude of 25 m. Finally, the proposed image redundancy removal method has been demonstrated as a feasible approach for reducing image mosaicking time (by 84%) and enhancing the quality of stitched images (by 14%). This research has the potential to reduce flight costs, improve image quality, and significantly enhance agricultural production efficiency.

Mosaic crack mapping of footings by convolutional neural networks

Cracks are the primary indicator informing the structural health of concrete structures. Frequent inspection is essential for maintenance, and automatic crack inspection offers a significant advantage, given its efficiency and accuracy. Previously, image-based crack detection systems have been utilized for individual images, yet these systems are not effective for large inspection areas. This paper thereby proposes an image-based crack detection system using a Deep Convolution Neural Network (DCNN) to identify cracks in mosaic images composed from UAV photos of concrete footings. UAV images are transformed into 3D footing models, from which the composite images are created. The CNN model is trained on 224 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 224 pixel patches, and training samples are augmented by various image transformation techniques. The proposed method is applied to localize cracks on composite images through the sliding window technique. The proposed VGG16 CNN detection system, with 95% detection accuracy, indicates superior performance to feature-based detection systems.

A radiometric and rotational invariant feature descriptor for multimodal remote sensing image matching using the monogenic signal

ABSTRACT Multimodal remote sensing image matching is still a challenging task, and its matching performance will highly influence the subsequent applications, such as image fusion, image mosaic and change detection. To tackle this problem, we propose a radiometric and rotational invariant descriptor (RRID) using the monogenic signal for multimodal image matching. First of all, a multiscale feature asymmetry measurement is proposed to obtain more reliable detected points, in which the multiscale feature asymmetry can be computed from the intermediate results from the monogenic signal; then a multiscale local phase orientation computation algorithm is designed for the main orientation assignment of each keypoint, in which the local phase orientation can also be provided by the monogenic signal construction; finally, a circular local window around each keypoint is extracted, a histogram of local phase orientations weighted by phase congruency is calculated for the log-polar feature description. RRID is compared with other state-of-the-art multimodal image matching methods on three publicly available datasets. Experimental results have demonstrated that our proposed method outperforms the compared methods in terms of precision, recall, and F1-measure criteria and also has achieved the lowest RMSEs.

A JWST Survey of the Supernova Remnant Cassiopeia A

We present initial results from a James Webb Space Telescope (JWST) survey of the youngest Galactic core-collapse supernova remnant, Cassiopeia A (Cas A), made up of NIRCam and MIRI imaging mosaics that map emission from the main shell, interior, and surrounding circumstellar/interstellar material (CSM/ISM). We also present four exploratory positions of MIRI Medium Resolution Spectrograph integral field unit spectroscopy that sample ejecta, CSM, and associated dust from representative shocked and unshocked regions. Surprising discoveries include (1) a weblike network of unshocked ejecta filaments resolved to ∼0.01 pc scales exhibiting an overall morphology consistent with turbulent mixing of cool, low-entropy matter from the progenitor’s oxygen layer with hot, high-entropy matter heated by neutrino interactions and radioactivity; (2) a thick sheet of dust-dominated emission from shocked CSM seen in projection toward the remnant’s interior pockmarked with small (∼1″) round holes formed by ≲0.″1 knots of high-velocity ejecta that have pierced through the CSM and driven expanding tangential shocks; and (3) dozens of light echoes with angular sizes between ∼0.″1 and 1′ reflecting previously unseen fine-scale structure in the ISM. NIRCam observations place new upper limits on infrared emission (≲20 nJy at 3 μm) from the neutron star in Cas A’s center and tightly constrain scenarios involving a possible fallback disk. These JWST survey data and initial findings help address unresolved questions about massive star explosions that have broad implications for the formation and evolution of stellar populations, the metal and dust enrichment of galaxies, and the origin of compact remnant objects.

渭河干流甘肃段河岸线空间变化数据集（2008、2015、2022年）

As the largest tributary of the Yellow River, the Weihe River has an important influence on the Yellow River in terms of both its flow and sediment load. Carrying out spatial and temporal evolution monitoring of the river shorelines is an important basis for grasping the hydrological changes of the river in the basin. In this study, we used the high-frequency and resource satellite image data of the Gansu section of the Weihe River in 2008, 2015 and 2022 as the base map, and extracted the river shorelines of the Gansu section of the Weihe River through the processing of image ortho-correction, alignment, fusion, mosaicing and cropping; combining with the image characteristics of the image, we adopted the method of interactive deciphering, including the Weihe River management range line, the Weihe River waterfront boundary line, and the Weihe River outer edge boundary line. Additionally, high-precision UAV aerial image data were utilized, integrating typical areas and important markers on the image base map data to ensure the reliability of river shoreline extraction. This dataset can provide data support for studying the hydrological dynamic monitoring and driving mechanisms of the Weihe River main stream. Furthermore, it can serve as basis for making informed decisions regarding the protection of river and lake shorelines and the ecological management of the river.

Three years of weekly DEMs, aerial orthomosaics and surveyed shoreline positions at Waikīkī Beach, Hawai‘i

In this dataset, we present 128 coastal surveys conducted between 2018 and 2021 at Kahaloa Beach, also known as the Royal Hawaiian Beach, in Waikīkī, Hawai‘i. Surveys were conducted on a near-weekly basis, providing a 0.5 m digital elevation model, an orthorectified image mosaic with 0.03 m resolution, and shoreline vectors at MHHW and MSL, along with a surveyed shoreline position for each survey. We captured overlapping images using a small Unoccupied Aerial System (sUAS), processing the imagery with photogrammetric software to produce orthomosaics and Digital Terrain Models (DTM). Simultaneously, the shoreline position and reference points for sUAS-derived products were surveyed using total station and rod-mounted surveying prism. A quality assessment of 424 randomly sampled points across two surveys showed normally distributed errors of DTM elevations (µ1 = 0.0060 m; σ1 = 0.0998 m; µ2 = 0.0035 m; σ2 = 0.0680). Elevation uncertainties were quantified as 95% confidence intervals (±0.0130 m and ±0.0095 m). These data are intended to encourage research on reef-fringed beaches and provide a dataset for evaluating the accuracy of satellite-derived shorelines at reef-fringed beaches.

Detection and Imaging of Corrosion Defects in Steel Structures Based on Ultrasonic Digital Image Processing

Corrosion is one of the critical factors leading to the failure of steel structures. Ultrasonic C-scans are widely used to identify corrosion damage. Limited by the range of C-scans, multiple C-scans are usually required to cover the whole component. Thus, stitching multiple C-scans into a panoramic image of the area under detection is necessary for interpreting non-destructive testing (NDT) data. In this paper, an image mosaic method for ultrasonic C-scan based on scale invariant feature transform (SIFT) is proposed. Firstly, to improve the success rate of registration, the difference in the probe starting position in two scans is used to filter the matching pairs of feature points obtained by SIFT. Secondly, dynamic programming methods are used to search for the optimal seam path. Finally, the pixels in the overlapping area are fused by fade-in and fade-out fusion along the seam line. The improved method has a higher success rate of registration and lower image distortion than the conventional method in the mosaic of ultrasonic C-scan images. Experimental results show that the proposed method can stitch multiple C-scan images of a testing block containing artificial defects into a panorama image effectively.

Autonomous Magnetic Navigation in Endoscopic Image Mosaics.

Endoscopes navigate within the human body to observe anatomical structures with minimal invasiveness. A major shortcoming of their use is their narrow field-of-view during navigation in large, hollow anatomical regions. Mosaics of endoscopic images can provide surgeons with a map of the tool's environment. This would facilitate procedures, improve their efficiency, and potentially generate better patient outcomes. The emergence of magnetically steered endoscopes opens the way to safer procedures and creates an opportunity to provide robotic assistance both in the generation of the mosaic map and in navigation within this map. This paper proposes methods to autonomously navigate magnetic endoscopes to 1) generate endoscopic image mosaics and 2) use these mosaics as user interfaces to navigate throughout the explored area. These are the first strategies, which allow autonomous magnetic navigation in large, hollow organs during minimally invasive surgeries. The feasibility of these methods is demonstrated experimentally both in vitro and ex vivo in the context of the treatment of twin-to-twin transfusion syndrome. This minimally invasive procedure is performed in utero and necessitates coagulating shared vessels of twin fetuses on the placenta. A mosaic of the vasculature in combination with autonomous navigation has the potential to significantly facilitate this challengingsurgery.

Minimum intensity projection of embossed quadrant-detection images for improved photoreceptor mosaic visualisation.

Non-confocal split-detection imaging reveals the cone photoreceptor inner segment mosaic in a plethora of retinal conditions, with the potential of providing insight to ageing, disease, and response to treatment processes, in vivo, and allows the screening of candidates for cell rescue therapies. This imaging modality complements confocal reflectance adaptive optics scanning light ophthalmoscopy, which relies on the waveguiding properties of cones, as well as their orientation toward the pupil. Split-detection contrast, however, is directional, with each cone inner segment appearing as opposite dark and bright semicircles, presenting a challenge for either manual or automated cell identification. Quadrant-detection imaging, an evolution of split detection, could be used to generate images without directional dependence. Here, we demonstrate how the embossed-filtered quadrant-detection images, originally proposed by Migacz etal. for visualising hyalocytes, can also be used to generate photoreceptor mosaic images with better and non-directional contrast for improved visualisation. As a surrogate of visualisation improvement between legacy split-detection images and the images resulting from the method described herein, we provide preliminary results of simple image processing routines that may enable the automated identification of generic image features, as opposed to complex algorithms developed specifically for photoreceptor identification, in pathological retinas.

Micromorphology and thematic micro-mapping reveal differences in the soil structuring traits of three arbuscular mycorrhizal fungi

Arbuscular mycorrhizal (AM) fungi contribute to soil structure, but little is known about the effect of individual fungal species on soil aggregation. In this study, the influence of 3 AM fungi species on soil aggregation in a Vitric Andosol was determined using physical, micromorphological, and imaging analyses. We used a pipe of polyvinyl chloride (PVC) with a six-way connector, which was filled with soil plus AM fungal inoculum (Funneliformis mosseae, Rhizophagus intraradices, Gigaspora gigantea or non-inoculated -control-). Then lateral pipe connectors (experimental units) were covered with mesh systems (0.5, 0.25, and 0.034 mm), and PVC tubes filled with sterile soil were connected laterally using a clamp. The greenhouse experiment consisted of four treatments each with 32 experimental units. Four experimental units of each treatment were separated and collected at different times during the year: three were used to determine water-stable aggregates (disturbed soils), and one was preserved (undisturbed soil) to elaborate soil thin sections. Thematic micro-maps were constructed with image mosaics from a whole soil thin section, and micromorphological analyses were conducted using spatial operators. Our results showed that AM fungi affect soil aggregation forming micro-aggregates and macro-aggregates of different sizes. The most significant effects were observed with F. mosseae > R. intraradices >Gi. gigantea > control. Aggregation hierarchy was observed in micromorphological analysis, where F. mosseae and R. intraradices start binding organo-mineral particles and microaggregates to form macroaggregates, modifying soil structure from intergrain (apedal= without peds) to crumb aggregates (pedal= with peds). Gigaspora gigantea only promoted macroaggregation, by associating with pumice particles. The two AM fungi from Glomeraceae possess similar morphology compared to that isolate belonging to Gigasporaceae, which explain in part, their differential contribution traits on soil aggregation, as highlighted by using together physical and micromorphological analyses of soil thin sections based on high-resolution image mosaics.

A flexible multi-temporal orthoimage mosaicking method based on dynamic variable patches

Orthoimage mosaicking plays an important role in remote sensing applications, such as environment monitoring and protection. However, the cloud coverage in remote sensing images often results in substantial loss of information and degradation of data quality, bringing lots of challenges to orthoimage mosaicking. Therefore, this paper proposes a flexible multi-temporal orthoimage mosaicking method based on dynamic variable patches. Different from most existing pixel-based mosaicking methods, the proposed method defines patch as the minimal processing unit for orthoimage mosaicking, and the size and position of each patch are dynamic variable. Patches from different images are rearranged under the image texture consistency constraint to generate the mosaic image. In the proposed method, the initial positions of available patches of all images are determined first, then the initial size of each patch is determined by the cloud coverage and the effective area of image. Subsequently, considering the inevitable misalignments between different orthoimages, the texture similarity between patches is utilized to select the most suitable patch, and the size of the patch is also dynamically adjusted to make the misalignments within each patch controllable. Finally, considering the color inconsistency in the overlapping areas of neighboring images, an area-weighted image blending algorithm is also presented to achieve invisible seams and smooth transitions in the final mosaic. Not only does the proposed method produce mosaic images with minimal cloud coverage areas by excluding cloud pixels as much as possible, but it is also flexible for the mosaicking of images with varying degrees of misalignments, which is achieved by dynamically adjusting the size and position of each patch while maintaining texture consistency constraint. Both simulated experiments and real data experiments are carried out to verify the performance of the proposed method. The experimental results demonstrate that the proposed method can generate the mosaic image with higher spatial continuity and radiometric consistency.

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges.

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.

Straight impact crater rim segments on Mercury

ABSTRACT The formation of straight impact crater rims is widely accepted to be influenced by preexisting lithospheric structure. We investigate the distribution and orientations of straight crater rim segments across Mercury. We devise a mapping workflow aimed at minimizing distortions of length and orientation that arise when working on projected image mosaics on a global scale and produce a global map of the rims of 7,145 impact craters with diameters between 20 and 400 km. We extract straight rim segments that maintain consistent orientations for at least 10 km to assess their frequency and orientation. Our dataset shows that 83% of craters have straight rim segments that show strong east–west orientations at the poles and weak north–south or random orientations at lower latitudes. This emphasizes the importance of lithospheric structure for impact cratering on Mercury. Our map dataset provides valuable insights for future investigations into the tectonic evolution of Mercury.

A Snapshot Multi-Spectral Demosaicing Method for Multi-Spectral Filter Array Images Based on Channel Attention Network

Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed. The raw images, which are captured by snapshot multi-spectral imaging systems, require a reconstruction procedure called demosaicing to estimate a fully defined multi-spectral image (MSI). With increasing spectral bands, the challenge of demosaicing becomes more difficult. Furthermore, the existing demosaicing methods will produce adverse artifacts and aliasing because of the adverse effects of spatial interpolation and the inadequacy of the number of layers in the network structure. In this paper, a novel multi-spectral demosaicing method based on a deep convolution neural network (CNN) is proposed for the reconstruction of full-resolution multi-spectral images from raw MSFA-based spectral mosaic images. The CNN is integrated with the channel attention mechanism to protect important channel features. We verify the merits of the proposed method using 5 × 5 raw mosaic images on synthetic as well as real-world data. The experimental results show that the proposed method outperforms the existing demosaicing methods in terms of spatial details and spectral fidelity.