Sökning: ": Synthetic Aperture Radar"

Visar resultat 21 - 25 av 66 uppsatser innehållade orden : Synthetic Aperture Radar.

  1. 21. Exploring Spatiotemporal Relationships between InSAR-derived Land Subsidence and Satellite-based Hydrological Variables

    Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

    Författare :Yixin Zhang; [2021]
    Nyckelord :InSAR; Shabestar; subsidence; groundwater; hydrological variables; LSTM; Sentinel-1A; Geomatics; Technology and Engineering; Earth and Environmental Sciences;

    Sammanfattning : Shabestar basin in the East Azerbaijan province, Northwest Iran, where irrigation is the main groundwater consumer, has experienced large-scale subsidence and groundwater deletion, which poses a threat to the local agricultural activities, economic development, and food security. With the emergency of mitigating the risk, satisfying future demand for groundwater, and improving resilience considering climate change, this study proposes a satellite-based approach to explore the spatio-temporal relationships between measured subsidence and hydrological variables in the basin to assist groundwater management strategy. LÄS MER

  3. 22. Deep Learning for Sea-Ice Classification on Synthetic Aperture Radar (SAR) Images in Earth Observation : Classification Using Semi-Supervised Generative Adversarial Networks on Partially Labeled Data

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Francesco Staccone; [2020]
    Nyckelord :Earth Observation; Classification; Deep Learning; Convolutional NeuralNetworks; Semi-Supervised Learning; GenerativeAdversarialNetworks; Jordobservation; Klassificering; Djupinlärning; IhopveckladeNeurala Nätverk; Halvövervakad Inlärning; Generativa Fientliga Nätverk;

    Sammanfattning : Earth Observation is the gathering of information about planet Earth’s system via Remote Sensing technologies for monitoring land cover types and their changes. Through the years, image classification techniques have been widely studied and employed to extract useful information from Earth Observation data such as satellite imagery. LÄS MER

  4. 23. Applicering av Long Short-Term Memory för prediktion av markdeformation på svensk järnväg : Utveckling av ett artificiellt neuralt nätverk för prediktion av kommande marksättningar på järnvägssträckan mellan Mölndal och Torrekulla

    Kandidat-uppsats, Högskolan Dalarna/Informatik

    Författare :William Wirsén; Martin Leijon; [2020]
    Nyckelord :Artificiell intelligens; artificial neural network; Long Short-Term Memory; InSAR; land subsidence; Recurrent Neural Network; Artificiell intelligens; artificiellt neurala nätverk; Long Short-Term Memory; InSAR; markdeformering; Recurrent Neural Network;

    Sammanfattning : The purpose of this study is to evaluate whether it is possible to predict future land subsidence on the railway line between Mölndal and Torrekulla. The prediction was made using Long Short-Term Memory; an artificial neural network with RNN architecture. LÄS MER

  5. 24. Assessing the accuracy for area-based tree species classification using Sentinel-1 C-band SAR data

    Master-uppsats, SLU/Dept. of Forest Resource Management

    Författare :Alberto Udali; [2019]
    Nyckelord :Sentinel-1; tree species; random forest; linear discrimi-nant analysis; classification;

    Sammanfattning : Forest type (FTY) and tree species classification (SPP) over the Remn-ingstorp test site were performed using ground-based field observations and remote sensing data sources. The field inventory for the forest estate and for the surrounding natural reserve of Eahagen was carried out in 2016. LÄS MER

  7. 25. Avalanche Visualisation Using Satellite Radar

    Kandidat-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Författare :Aron Widforss; [2019]
    Nyckelord :snow science; avalanche detection; satellite SAR; avanor;

    Sammanfattning : Avalanche forecasters need precise knowledge about avalanche activity in large remote areas. Manual methods for gathering this data have scalability issues. Synthetic aperture radar satellites may provide much needed complementary data. LÄS MER