Sökning: "NIR"

Visar resultat 16 - 20 av 114 uppsatser innehållade ordet NIR.

  1. 16. Development of a Level-0 Geoprocessing Platform for a Multispectral Remote Sensing Payload

    Master-uppsats, KTH/Lättkonstruktioner, marina system, flyg- och rymdteknik, rörelsemekanik

    Författare :Sergio Santiago Bernabeu Peñalba; [2022]
    Nyckelord :Infrared imagery; VIS; NIR; LWIR; geoprocessor; geolocation; quaternions; Aistech Space; image processing; ORB; RANSAC; Sentinel; Landsat; Infraröda bilder; VIS; NIR; LWIR; geoprocessor; geolokalisering; kvarternioner; Aistech Space; bildbehandling; ORB; RANSAC; Sentinel; Landsat;

    Sammanfattning : This thesis presented an overview of the development of a geolocating algorithm as part of a geoprocessor for raw satellite imagery. This algorithm was devised for and limited by the specifications of a state-of-the-art multispectral telescope designed by Aistech Space, hosted onboard the Guardian spacecraft, which will observe Earth through the visible, near infrared, and thermal infrared bands of the electromagnetic spectrum. LÄS MER

  3. 17. Microsatellite Constellation for Wildfire Monitoring

    Master-uppsats, KTH/Geoinformatik

    Författare :Citlali Bruce Rosete; [2022]
    Nyckelord :microsatellite; shortwave infrared sensor; active fire monitoring; burned area detection; satellite constellation; mikrosatellit; kortvågig infraröd sensor; aktiv brandövervakning; detektion av brandhärjat område; satellitkonstellation;

    Sammanfattning : I flera års tid har antalet svåra och okontrollerade skogsbränder ökat i antal. Det finns ett behov av att detektera skogsbränder med hjälp av satelliter som har högre tidsupplösning samt högre geometrisk upplösning än de satelliter som är i bruk idag. LÄS MER

  4. 18. Prediction of flue gas properties using artificial intelligence : Application of supervised machine learning by utilization of Near-Infrared Spectroscopy on solid biofuels

    Uppsats för yrkesexamina på avancerad nivå, Mälardalens universitet/Akademin för ekonomi, samhälle och teknik

    Författare :Bashe Abdirahman Hussein; Emran Samimi; [2022]
    Nyckelord :Artificial Intelligence; Machine learning; Near-Infrared spectroscopy; Solid biofuels; Fuel element; Flue gas composition; CCS;

    Sammanfattning : This degree project studies implementation and comparison of different AI models to predict (1) solid biofuel elements including carbon, hydrogen, nitrogen, and oxygen as well as moisture content, ash content, and higher heating value (HHV) of the fuel and (2) flue gas compositions such as concentration of carbon dioxide, carbon monoxide, nitrogen, nitrogen oxides, and water content using near-infrared spectroscopy and chemometric approaches. The study executes these predictions by simulating the operation of a combined heat and power plant (CHP) that is equipped with carbon capture and storage (CCS). LÄS MER

  5. 19. Controls of the partitioning of nitrate between DNRA and denitrifiers

    Master-uppsats, SLU/Dept. of Forest Mycology and Plant Pathology

    Författare :Frida Lindvall; [2022]
    Nyckelord :DNRA; denitrification; microcosm; experiment; arable soil;

    Sammanfattning : In arable soils, the importance of denitrification, a respiratory process where nitrate (NO3-) is stepwisely reduced to nitrogen gas and nitrous oxide (N2O) is well established. More recently has dissimilatory nitrate reduction to ammonium (DNRA) gained interest as its importance in agricultural soils might have been largely overlooked. LÄS MER

  7. 20. Exploring Diversity of Spectral Data in Cloud Detection with Machine Learning Methods : Contribution of Near Infrared band in improving cloud detection in winter images

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

    Författare :Nakita Sunil Oza; [2022]
    Nyckelord :Remote sensing; Multispectral imaging; Cloud detection; Data diversity; Deep learning; Fjärranalys; Multispektral bildbehandling; Molndetektion; Datadiversitet; Djupinlärning;

    Sammanfattning : Cloud detection on satellite imagery is an essential pre-processing step for several remote sensing applications. In general, machine learning based methods for cloud detection perform well, especially the ones based on deep learning as they consider both spatial and spectral features of the input image. LÄS MER