Sökning: "synthetic aperture radar imaging"

Visar resultat 16 - 18 av 18 avhandlingar innehållade orden synthetic aperture radar imaging.

  1. 16. Deep Learning for Active Fire Detection Using Multi-Source Satellite Image Time Series

    Författare :Yu Zhao; Yifang Ban; Andrea Nascetti; Josephine Sullivan; Cartalis Constantinos; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Wildfire; Remote Sensing; Active Fire Detection; GOES-R ABI; Suomi-NPP VIIRS; Image Segmentation; Deep Learning; Gated Recurrent Units GRU ; Transformer.; Vilda Bränder; Fjärranalys; Aktiv Branddetektering; GOES- R ABI; Suomi-NPP VIIRS; Bildsegmentering; Djupinlärning; Gated Recurrent Units GRU ; Transformer; Geoinformatik; Geoinformatics;

    Sammanfattning : In recent years, climate change and human activities have caused increas- ing numbers of wildfires. Earth observation data with various spatial and temporal resolutions have shown great potential in detecting and monitoring wildfires. LÄS MER

  3. 17. Sea Ice and Ocean Environmental Applications of Spaceborn SAR

    Författare :Sverre Thune Dokken; Chalmers tekniska högskola; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Radarsat; SSM I; ERS-1 2; ocean applications; sea ice applications; numerical models; SAR; Arctic Ocean; Envisat; climate change;

    Sammanfattning : The worldwide focus on our changing environment has led to an increased need to observe and characterise a range of environmental phenomena. The Arctic Ocean is particularly sensitive to climate variations. LÄS MER

  4. 18. Supervised and Unsupervised Deep Learning Models for Flood Detection

    Författare :Ritu Yadav; Yifang Ban; Andrea Nascetti; Nicolas Audebert; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Floods; Remote Sensing; Sentinel-1 SAR; Segmentation; Change Detection; DEM; Data Fusion; Time Series; Deep Learning; Unsupervised Learning; Contrastive Learning; Self-Attention; Convolutional LSTM; Variational AutoEncoder VAE ; Geoinformatik; Geoinformatics;

    Sammanfattning : Human civilization has an increasingly powerful influence on the earthsystem. Affected by climate change and land-use change, floods are occurringacross the globe and are expected to increase in the coming years. Currentsituations urge more focus on efficient monitoring of floods and detecting impactedareas. LÄS MER