Preliminary spectral and geological analyses of the Degas crater on Mercury - supporting the SIMBIO-SYS instrument onboard BepiColombo

2021 ◽  
Author(s):  
Nicolas Bott ◽  
Océane Barraud ◽  
Laura Guzzetta
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Mineralogical Composition ◽  
Geological Mapping ◽  
Absorption Bands ◽  
Spectral Parameters ◽  
Very High Spatial Resolution ◽  
Multispectral Analysis

<p>The BepiColombo spacecraft was launched in October 19th, 2018 (local time) towards Mercury, carrying 16 instruments in two orbiters (MPO and MMO). Among this impressive set of devices, the SIMBIO-SYS (Spectrometer and Imagers for MPO BepiColombo Integrated Observatory SYStem) instrument [Cremonese et al., 2020] will map at an unprecedented high resolution the surface of the innermost planet of the Solar system, thanks to 3 cameras: STC (Stereo Channel), a stereo camera; HRIC (High spatial Resolution Imaging Channel), a multispectral camera with a very high spatial resolution; VIHI (Visible Infrared Hyperspectral Imager channel), a hyperspectral imager to with a good spectral resolution and a good S/N ratio. The last one aims to map the global mineralogical composition of Mercury, which has not yet been precisely determined due to the absence of diagnostic absorption bands in the remote sensing data of the previous MESSENGER mission [Izenberg et al., 2014]. The choice and the list of targets SIMBIO-SYS will have to analyse are still in progress and are continuously updated. Therefore, preliminary studies of potential targets of interest can be very useful to support their selection.</p><p>For that purpose, we started investigating a particular crater, Degas, which occurs in the Shakespeare quadrangle (H-03) [Guzzetta et al., 2017; Bott et al., 2019], located at mid-latitudes of the northern hemisphere of Mercury (37.08 ◦ N - 232.66 ◦ E). Its well-preserved ray system of ejecta are a strong hint in favor of its chronostratigraphic classification as a Kuiperian (-1 Gyr – today) crater [Banks et al., 2017]. By using MESSENGER data, we analysed the Degas crater with a three-fold approch: a multispectral analysis based on MDIS-WAC data have been combined with a spectroscopic analysis of MASCS data and a geological analysis based on MDIS-NAC images. Here, we would like to present the first outputs of our works, including a set of color and monochrome mosaics, spectral parameters maps and spectra of each kind of terrain identified with the mosaics, and the first results of the high-resolution geological mapping of the Degas crater performed on a NAC images mosaic of 23 m/pixel. Other findings and initial discussions will be presented during the virtual talk.</p><p>Acknowledgements: This work is partly supported by the Centre National d' Études Spatiales. We gratefully acknowledge funding from the Italian Space Agency (ASI) under ASI-INAF agreement2017-47-H.0. The authors acknowledge the use of MESSENGER data.</p>

scholarly journals INTEGRATION OF REMOTE SENSING DATA AND BASIC GEODATA AT DIFFERENT SCALE LEVELS FOR IMPROVED LAND USE ANALYSES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 85-89 ◽  
Author(s):  
G. Waldhoff ◽  
S. Eichfuss ◽  
G. Bareth
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
Geographical Information Systems ◽  
High Spatial Resolution ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Sensing Data ◽  
Very High Spatial Resolution

The classification of remote sensing data is a standard method to retrieve up-to-date land use data at various scales. However, through the incorporation of additional data using geographical information systems (GIS) land use analyses can be enriched significantly. In this regard, the Multi-Data Approach (MDA) for the integration of remote sensing classifications and official basic geodata for a regional scale as well as the achievable results are summarised. On this methodological basis, we investigate the enhancement of land use analyses at a very high spatial resolution by combining WorldView-2 remote sensing data and official cadastral data for Germany (the Automated Real Estate Map, ALK). Our first results show that manifold thematic information and the improved geometric delineation of land use classes can be gained even at a high spatial resolution.

scholarly journals Performance evaluation of super resolution algorithms in generating high resolution images using MSE and PSNR

2021 ◽  
Vol 10 (02) ◽  
pp. 25284-25291
Author(s):  
Palani Murugan ◽  
Vivek Kumar Gautam ◽  
V. Ramanathan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Optical Systems ◽  
High Resolution Data ◽  
Sensing Data ◽  
Resolution Data

In recent days, requirement of high spatial resolution remote sensing data in various fields has increased tremendously.  High resolution satellite remote sensing data is obtained with long focal length optical systems and low altitude. As fabrication of high-resolution optical system and accommodating on the satellite is a challenging task, various alternate methods are being explored to get high resolution imageries. Alternately the high-resolution data can be obtained from super resolution techniques. The super resolution technique uses single or multiple low-resolution mis-registered data sets to generate high resolution data set.  Various algorithms are employed in super resolution technique to derive high spatial resolution. In this paper we have compared two methods namely overlapping and interleaving methods and their capability in generating high resolution data are presented.

Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures

2017 ◽  
Vol 17 (3) ◽  
pp. 514-531 ◽  
Author(s):  
Yongchao Yang ◽  
Charles Dorn ◽  
Tyler Mancini ◽  
Zachary Talken ◽  
James Theiler ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Mode Shape ◽  
High Spatial Resolution ◽  
Mode Shapes ◽  
Vibration Measurements ◽  
Full Field ◽  
Very High Spatial Resolution ◽  
Output Only ◽  
Very High

Detecting damage in structures based on the change in their dynamics or modal parameters (modal frequencies and mode shapes) has been extensively studied for three decades. The success of such a global, passive, vibration-based method in field applications, however, has long been hindered by the bottleneck of low spatial resolution vibration sensor measurements. The primary reason is that damage typically initiates and develops in local regions that need to be captured and characterized by very high spatial resolution vibration measurements and modal parameters (mode shapes), which are extremely difficult to obtain using traditional vibration measurement techniques. For example, accelerometers and strain-gauge sensors are typically placed at a limited number of discrete locations, providing low spatial resolution vibration measurements. Laser vibrometers provide high-resolution measurements, but are expensive and make sequential measurements that are time- and labor-consuming. Recently, digital video cameras—which are relatively low cost, agile, and able to provide high spatial resolution, simultaneous, pixel measurements—have emerged as a promising tool to achieve full-field, high spatial resolution vibration measurements. Combined with advanced vision processing and unsupervised machine algorithms, a new method has recently been developed to blindly and efficiently extract the full-field, high-resolution, dynamic parameters from the video measurements of an operating, output-only structure. This work studies the feasibility of performing damage detection using the full-field, very high spatial resolution mode shape (of the fundamental mode) blindly extracted from the video of the operating (output-only) structure without any knowledge of reference (healthy) structural information. A spatial fractal dimension analysis is applied on the full-field mode shape of the damaged structure to detect damage-induced irregularity. Additionally, the equivalence between the fractal dimension and the squared curvature (modal strain energy) of the mode shape curve, when of high spatial resolution, is mathematically derived. Laboratory experiments are conducted on bench-scale structures, including a building structure and a cantilever beam, to validate the approach. The results illustrate that using the full-field, very high-resolution mode shape enables detection of minute, non-visible, damage in a global, completely passive sensing manner, which was previously not possible to achieve.

scholarly journals Detailed geological mapping of the Olon-Ovoot gold-ore cluster (South Mongolia) based on the interpretation of satellite imagery of medium and high spatial resolution.

2019 ◽  
pp. 59-71
Author(s):  
M. V. Zadorozhnyy ◽  
I. D. Zolnikov ◽  
N. V. Glushkova
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Geological Structure ◽  
Google Earth ◽  
Geological Mapping ◽  
Geological Interpretation ◽  
Gold Ore ◽  
Sentinel 2

Detailed geological mapping of Olon-Ovoot gold-ore cluster (South Mongolia) on the basis of interpretation of satellite imagery of medium and high spatial resolution The article presents the results of geological interpretation the territory of the Olon-Ovoot ore cluster by space imagery of medium and high spatial resolution. A Sentinel-2 imagery, chosen for interpretation, was orthorectified and reduced to a common spatial resolution (10m) The iron-hydroxid and ferrous-silicates indices in Sentinel-2 imagery were used to detect the perspective gold-bearing objects. The sub-pixel structure of the imagery Sentinel-2 were analyzed by means of satellite imagery of high spatial resolution by Google Earth for detecting areas concentration of the quartz-carbonate veins. The study of the spectral domain in high-resolution imagery not necessary for detecting lineaments by structural and morphological interpretation. The interpretation of the remote sensing data provide a unique opportunity to substantial specify the geological structure of the territory and change the level of mapping from the scale of 1 : 200 000 to the scale of 1 : 20 000 for the perspective areas. The integration of satellite images of different functional scale provided an tenfold increase for some geological objects (for example dikes). Detailed mapping of the territory allowed to come for geoinformation modeling of geological structural elements and predictive indicators.

scholarly journals How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

2020 ◽  
Vol 12 (3) ◽  
pp. 417 ◽  
Author(s):  
Xin Zhang ◽  
Liangxiu Han ◽  
Lianghao Han ◽  
Liang Zhu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Sensing Data

Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models.

scholarly journals High spatial resolution photo mosaicking for the monitoring of coralligenous reefs

Coral Reefs ◽  
2021 ◽  
Author(s):  
E. Casoli ◽  
D. Ventura ◽  
G. Mancini ◽  
D. S. Pace ◽  
A. Belluscio ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Low Cost ◽  
Pixel Resolution ◽  
Recording Apparatus ◽  
Assemblage Composition ◽  
Very High Spatial Resolution ◽  
Vertical Walls ◽  
Non Destructive ◽  
Very High

AbstractCoralligenous reefs are characterized by large bathymetric and spatial distribution, as well as heterogeneity; in shallow environments, they develop mainly on vertical and sub-vertical rocky walls. Mainly diver-based techniques are carried out to gain detailed information on such habitats. Here, we propose a non-destructive and multi-purpose photo mosaicking method to study and monitor coralligenous reefs developing on vertical walls. High-pixel resolution images using three different commercial cameras were acquired on a 10 m2 reef, to compare the effectiveness of photomosaic method to the traditional photoquadrats technique in quantifying the coralligenous assemblage. Results showed very high spatial resolution and accuracy among the photomosaic acquired with different cameras and no significant differences with photoquadrats in assessing the assemblage composition. Despite the large difference in costs of each recording apparatus, little differences emerged from the assemblage characterization: through the analysis of the three photomosaics twelve taxa/morphological categories covered 97–99% of the sampled surface. Photo mosaicking represents a low-cost method that minimizes the time spent underwater by divers and capable of providing new opportunities for further studies on shallow coralligenous reefs.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

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