Добірка наукової літератури з теми "Sub-Meter resolution imagery"
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Статті в журналах з теми "Sub-Meter resolution imagery"
Almeida, Luís, Rafael Almar, Erwin Bergsma, Etienne Berthier, Paulo Baptista, Erwan Garel, Olusegun Dada, and Bruna Alves. "Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery." Remote Sensing 11, no. 5 (March 12, 2019): 590. http://dx.doi.org/10.3390/rs11050590.
Повний текст джерелаTian, 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.
Повний текст джерелаManos, 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 (June 6, 2022): 2719. http://dx.doi.org/10.3390/rs14112719.
Повний текст джерелаXu, 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 (March 8, 2023): 1499. http://dx.doi.org/10.3390/rs15061499.
Повний текст джерелаYalcin, 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.
Повний текст джерелаFarella, E. M., F. Remondino, C. Cahalane, R. Qin, A. M. Loghin, M. Di Tullio, N. Haala, and J. Mills. "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.
Повний текст джерелаUdawalpola, 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.
Повний текст джерелаHöschle, Caroline, Hannah C. Cubaynes, Penny J. Clarke, Grant Humphries, and Alex Borowicz. "The Potential of Satellite Imagery for Surveying Whales." Sensors 21, no. 3 (February 1, 2021): 963. http://dx.doi.org/10.3390/s21030963.
Повний текст джерелаWohlfeil, 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.
Повний текст джерелаBelart, Joaquín M. C., Etienne Berthier, Eyjólfur Magnússon, Leif S. Anderson, Finnur Pálsson, Thorsteinn Thorsteinsson, Ian M. Howat, Guðfinna Aðalgeirsdóttir, Tómas Jóhannesson, and Alexander H. Jarosch. "Winter mass balance of Drangajökull ice cap (NW Iceland) derived from satellite sub-meter stereo images." Cryosphere 11, no. 3 (June 30, 2017): 1501–17. http://dx.doi.org/10.5194/tc-11-1501-2017.
Повний текст джерелаДисертації з теми "Sub-Meter resolution imagery"
Li, Sizhuo. "Deep Learning for Forest Resource Mapping from Sub-Meter Resolution Imagery : Technical Insights and Methodologies." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASJ015.
Повний текст джерелаDeep learning has transformed numerous fields so far, propelled by improved algorithms and increased data accessibility. Remote sensing, in particular, offers significant opportunities for vision applications with direct socio-ecological implications. Forests represent a major environmental component, offering essential functions including climate regulation, biodiversity preservation, and interaction with living creatures. Departing from studies that reveal forest patterns using coarse resolution imagery or structural sensors like LiDAR, this thesis explores sub-meter resolution imagery - human-interpretable and highly detailed visual data covering the Earth. Forest attributes of different types are investigated, varying from tree characteristics to forest height and biomass. Technically, this thesis starts with a vanilla setup of in-domain semantic segmentation, delves into the regression of structural attributes, and ends with cross-domain adaptation. This brings insights into the learning capacity of vision models in the context of natural scene image understanding. The first part of the thesis introduces a deep learning framework to count, locate, and estimate the height of individual trees from aerial images at the national scale. An attention UNet is utilized to delineate individual tree crowns and count trees with point supervision. Tree height is estimated from optical imagery by learning a mapping from visual cues to canopy heights projected from LiDAR point clouds. Results are compared against field data to assess the practical values of the framework as supplementary data to support digitized national forest management. This study highlights the capacity of deep learning in characterizing visually interpretable forest and tree structures. Yet, challenges persist in learning more complex patterns from the optical data. This motivates the second study on forest biomass at stand level, a structural measure of forests typically collected on the ground. At larger scales, predominating methods apply statistical or machine learning models on multispectral imagery, often complemented by height data and calibrated with field data. To our knowledge, we are the first to demonstrate that stand-level biomass can be directly learned from sub-meter resolution RGB imagery, rich in forest and tree details, using convolutional neural networks and field data. This provides avenues for efficient and accurate quantification of forest biomass, a critical indicator of forest resources that supports nature preservation and carbon-neutral commitments. The first two studies employ deep learning systems to quantify forest attributes given a specific dataset, which follows the principal in-domain assumption of machine learning. Yet, degraded performance is often observed when applied to out-of-distribution data, a common scenario in practice. The third study aims to address the domain shift issue, exploring whether deep learning models trained on one dataset can be quickly adapted to new datasets with marginal efforts. We release a new dataset consisting of sub-meter resolution optical imagery collected in five countries and assess the cross-domain adaptability of various image-level regression tasks, including tree cover, total tree count, and average canopy height. By enforcing ordered embedding space during training, models are effectively prepared for later adaptation in source-free low-shot setups. Overall, this thesis introduces a collection of deep learning systems tailored for forest resource mapping with depth into technical and applied insights, contributing to sustainable management efforts for a greener future
Deep learning har hidtil transformeret talrige områder, drevet af forbedrede algoritmer og øgettilgængelighed af data. Fjernregistrering tilbyder betydelige muligheder for visionsapplikationermed direkte socioøkologiske implikationer. Skoveudgør en vigtig miljømæssig komponent og tilbyder essentielle funktioner, herunder klimaregulering, bevarelse af biodiversitet og interaktion medlevende væsener. Afgang fra studier, der afslørerskovmønstre ved hjælp af grovopløsningsbilledereller struktursensorer som LiDAR, udforsker denneafhandling sub-meteropløsningsbilleder - menneskefortolkelige og detaljerede visuelle data, derdækker Jorden. Forskellige skovattributter undersøges, lige fra trækarakteristika til skovhøjdeog biomasse. Teknisk set starter denne afhandling med en grundlæggende opsætning af semantisk segmentering inden for domænet, går videretil regression af strukturelle attributter og sluttermed tværfaglig tilpasning. Dette giver indsigt i visionmodellers indlæringskapacitet i konteksten afforståelse af naturscener. Afhandlingens førstedel introducerer et dybtlæringsrammeværk til attælle, lokalisere og estimere højden af individuelle træer fra luftfotos på nationalt plan. En opmærksomhed UNet bruges til at afgrænse individuelle trækroner og tælle træer med punktsupervision. Træhøjde estimeres fra optisk billedmaterialeved at lære en afbildning fra visuelle ledetråde tilkronhøjder projiceret fra LiDAR-punktskyer. Resultater sammenlignes med feltdatabaser for at vurdere rammeværkets praktiske værdier som supplement til understøttelse af digitaliseret nationalskovforvaltning. Denne undersøgelse fremhæverdybtlæringens evne til at karakterisere visuelt fortolkede skov- og træstrukturer. Dog vedvarerudfordringer i at lære mere komplekse mønstrefra det optiske data. Dette motiverer den anden undersøgelse af skovbiomasse på bestandsniveau, en strukturel måling af skove, der typiskindsamles på jorden. På større skalaer anvender dominerende metoder statistiske eller maskinlæringsmodeller på multispektralt billedmateriale,ofte suppleret med højdedata og kalibreret medfeltdatabaser. Efter vores viden er vi de førstetil at demonstrere, at bestandsniveauets biomassedirekte kan læres fra sub-meteropløsnings-RGBbilledmateriale, rigt på skov- og trædetaljer, vedhjælp af konvolutionelle neurale netværk og feltdatabaser. Dette giver muligheder for effektiv og præcis kvantificering af skovbiomasse, enkritisk indikator for skovressourcer, der støtternaturbevarelse og klimaneutrale forpligtelser. Deførste to studier benytter dybtlæringssystemer tilat kvantificere skovattributter ud fra et specifikt datasæt, hvilket følger den grundlæggendeantagelse om maskinlæring inden for domænet.Dog observeres der ofte nedsat præstation, nårdet anvendes på data uden for distributionen,en almindelig situation i praksis. Det tredjestudie sigter mod at adressere domæneskiftproblemet ved at undersøge, om dybtlæringsmodeller trænet på ét datasæt hurtigt kan tilpassestil nye datasæt med marginale bestræbelser.Vi frigiver et nyt datasæt bestående af submeteropløsnings optisk billedmateriale indsamleti fem lande og vurderer krydsdomænet tilpasningsevne for forskellige billedniveau-regressionstasks,herunder trædække, samlet trætælling og gennemsnitlig krones højde. Ved at håndhæve en ordnetindlejret rum under træning forberedes modellereffektivt til senere tilpasning i kildefrie lavskudsopsætninger. Overordnet introducerer denne afhandling en samling dybtlæringssystemer skræddersyet til kortlægning af skovressourcer med dybdei tekniske og anvendte indsigter, hvilket bidrager tilbæredygtige forvaltningsindsatser for en grønnerefremtid
Частини книг з теми "Sub-Meter resolution imagery"
Mozgovoy, D. K., D. V. Kapulin, D. N. Svinarenko, A. I. Sablinskii, T. N. Yamskikh, and R. Yu Tsarev. "Automated Detection of Anthropogenic Changes in Municipal Infrastructure with Satellite Sub-meter Resolution Imagery." In Advances in Intelligent Systems and Computing, 362–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51974-2_35.
Повний текст джерелаMozgovoy, D. K., D. V. Kapulin, D. N. Svinarenko, T. N. Yamskikh, A. A. Chikizov, and R. Yu Tsarev. "Geometry-Based Automated Recognition of Objects on Satellite Images of Sub-meter Resolution." In Advances in Intelligent Systems and Computing, 371–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51974-2_36.
Повний текст джерелаТези доповідей конференцій з теми "Sub-Meter resolution imagery"
Mottus, Matti, Matthieu Molinier, Eelis Halme, Hai Cu, and Jorma Laaksonen. "Patch Size Selection for Analysis of Sub-Meter Resolution Hyperspectral Imagery of Forests." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554257.
Повний текст джерелаHill, Samuel L., and Peter Clemens. "Miniaturization of sub-meter resolution hyperspectral imagers on unmanned aerial systems." In SPIE Sensing Technology + Applications, edited by David P. Bannon. SPIE, 2014. http://dx.doi.org/10.1117/12.2054822.
Повний текст джерелаPesaresi, Martino, Thomas Kemper, Lionel Gueguen, and Pierre Soille. "Automatic information retrieval from meter and sub-meter resolution satellite image data in support to crisis management." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5653039.
Повний текст джерела