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Bibliografias temáticas / Sub-Meter resolution imagery

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Literatura científica selecionada sobre o tema "Sub-Meter resolution imagery"

Autor: Grafiati

Publicado: 31 de agosto de 2024

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Artigos de revistas sobre o assunto "Sub-Meter resolution imagery"

1

Almeida, Luís, Rafael Almar, Erwin Bergsma, et al. "Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery." Remote Sensing 11, no. 5 (2019): 590. http://dx.doi.org/10.3390/rs11050590.

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High spatial resolution coastal Digital Elevation Models (DEMs) are crucial to assess coastal vulnerability and hazards such as beach erosion, sedimentation, or inundation due to storm surges and sea level rise. This paper explores the possibility to use high spatial-resolution Pleiades (pixel size = 0.7 m) stereoscopic satellite imagery to retrieve a DEM on sandy coastline. A 40-km coastal stretch in the Southwest of France was selected as a pilot-site to compare topographic measurements obtained from Pleiades satellite imagery, Real Time Kinematic GPS (RTK-GPS) and airborne Light Detection a

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Tian, J., X. Zhuo, X. Yuan, C. Henry, P. d’Angelo, and T. Krauss. "APPLICATION ORIENTED QUALITY EVALUATION OF GAOFEN-7 OPTICAL STEREO SATELLITE IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2022 (May 17, 2022): 145–52. http://dx.doi.org/10.5194/isprs-annals-v-1-2022-145-2022.

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Abstract. GaoFen-7 (GF-7) satellite mission is further expanding the very high resolution 3D mapping application. Carrying the first civilian Chinese sub-meter resolution stereo satellite sensors, GF-7 satellite was launched on November 7, 2019. With 0.65 meter resolution on backward view and 0.8 meter resolution forward view, GF-7 has been designed to meet the demand of natural resource monitoring, land surveying, and other mapping applications in China. The use of GF-7 for 3D city reconstruction is unfortunately restricted by the fixed large stereo view angle of forward and backward cameras

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Manos, Elias, Chandi Witharana, Mahendra Rajitha Udawalpola, Amit Hasan, and Anna K. Liljedahl. "Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery." Remote Sensing 14, no. 11 (2022): 2719. http://dx.doi.org/10.3390/rs14112719.

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Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While im

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Xu, Huiyao, Jia Song, and Yunqiang Zhu. "Evaluation and Comparison of Semantic Segmentation Networks for Rice Identification Based on Sentinel-2 Imagery." Remote Sensing 15, no. 6 (2023): 1499. http://dx.doi.org/10.3390/rs15061499.

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Efficient and accurate rice identification based on high spatial and temporal resolution remote sensing imagery is essential for achieving precision agriculture and ensuring food security. Semantic segmentation networks in deep learning are an effective solution for crop identification, and they are mainly based on two architectures: the commonly used convolutional neural network (CNN) architecture and the novel Vision Transformer architecture. Research on crop identification from remote sensing imagery using Vision Transformer has only emerged in recent times, mostly in sub-meter resolution o

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Yalcin, I., S. Kocaman, S. Saunier, and C. Albinet. "RADIOMETRIC QUALITY ASSESSMENT FOR MAXAR HD IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 797–804. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-797-2021.

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Abstract. The requirement for very high-resolution satellite imagery by different applications has been increasing continuously. Several commercial and government-supported missions provide sub-meter spatial resolutions from optical sensors aboard Earth Observation (EO) satellites. The MAXAR satellite constellation acquires images with up to 30 cm Ground Sampling Distances (GSDs); and the High-Definition (HD) image production technology developed by MAXAR doubles the resolution by using artificial intelligence methods. Although the spatial resolution is one of the most important image quality

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6

Farella, E. M., F. Remondino, C. Cahalane, et al. "GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W3-2023 (October 19, 2023): 47–54. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-47-2023.

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Abstract. In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VH

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7

Udawalpola, M., A. Hasan, A. K. Liljedahl, A. Soliman, and C. Witharana. "OPERATIONAL-SCALE GEOAI FOR PAN-ARCTIC PERMAFROST FEATURE DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 175–80. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-175-2021.

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Abstract. Regional extent and spatiotemporal dynamics of Arctic permafrost disturbances remain poorly quantified. High spatial resolution commercial satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. The imagery is still largely underutilized, and value-added Arctic science products are rare. Knowledge discovery through artificial intelligence (AI),

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8

Höschle, Caroline, Hannah C. Cubaynes, Penny J. Clarke, Grant Humphries, and Alex Borowicz. "The Potential of Satellite Imagery for Surveying Whales." Sensors 21, no. 3 (2021): 963. http://dx.doi.org/10.3390/s21030963.

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The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computatio

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9

Wohlfeil, J., H. Hirschmüller, B. Piltz, A. Börner, and M. Suppa. "FULLY AUTOMATED GENERATION OF ACCURATE DIGITAL SURFACE MODELS WITH SUB-METER RESOLUTION FROM SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B3 (July 23, 2012): 75–80. http://dx.doi.org/10.5194/isprsarchives-xxxix-b3-75-2012.

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10

Belart, Joaquín M. C., Etienne Berthier, Eyjólfur Magnússon, et al. "Winter mass balance of Drangajökull ice cap (NW Iceland) derived from satellite sub-meter stereo images." Cryosphere 11, no. 3 (2017): 1501–17. http://dx.doi.org/10.5194/tc-11-1501-2017.

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Abstract. Sub-meter resolution, stereoscopic satellite images allow for the generation of accurate and high-resolution digital elevation models (DEMs) over glaciers and ice caps. Here, repeated stereo images of Drangajökull ice cap (NW Iceland) from Pléiades and WorldView2 (WV2) are combined with in situ estimates of snow density and densification of firn and fresh snow to provide the first estimates of the glacier-wide geodetic winter mass balance obtained from satellite imagery. Statistics in snow- and ice-free areas reveal similar vertical relative accuracy (< 0.5 m) with and without gro

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