Sökning: "multispectral imagery"

Visar resultat 1 - 5 av 23 uppsatser innehållade orden multispectral imagery.

  1. 1. Land cover classification using machine-learning techniques applied to fused multi-modal satellite imagery and time series data

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

    Författare :Anastasia Sarelli; [2024]
    Nyckelord :Geography; GIS; Land Cover Classification; Landsat; Machine Learning; Earth and Environmental Sciences;

    Sammanfattning : Land cover classification is one of the most studied topics in the field of remote sensing, involving the use of data from satellite sensors to analyze and categorize different land surface types. There are numerous satellite products available, each offering different spatial, spectral, and temporal resolutions. LÄS MER

  3. 2. Reliable Detection of Water Areas in Multispectral Drone Imagery : A faster region-based CNN model for accurately identifying the location of small-scale standing water bodies

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

    Författare :Shengyao Shangguan; [2023]
    Nyckelord :Water Detection; Faster region-based convolutional neural networks; Multiple images; Convolutional neural networks; Random Forest; Vattendetektering; Snabbare regionbaserade konvolutionella neurala nätverk; Flera bilder; Konvolutionella neurala nätverk; Random Forest;

    Sammanfattning : Dengue and Zika are two arboviral viruses that affect a significant portion of the world population. The principal vector species of both viruses are Aedes aegypti and Aedes albopictus mosquitoes. They breed in very slow flowing or standing pools of water. LÄS MER

  4. 3. Forest Aboveground Biomass Monitoring in Southern Sweden Using Random Forest Modelwith Sentinel-1, Sentinel-2, and LiDAR Data

    Magister-uppsats, Högskolan i Gävle/Samhällsbyggnad

    Författare :Wan Ni Lin; [2023]
    Nyckelord :Aboveground biomass; Sentinel-1; Sentinel-2; LiDAR; random forest; GEE;

    Sammanfattning : Monitoring carbon stock has emerged as a critical environmental problem among several worldwide organizations and collaborations in the context of global warming and climate change. This study seeks to provide a remote sensing solution based on three types of data, to explore the feasibility and reliability of estimating aboveground biomass (AGB) in order to improve the efficiency of monitoring carbon stock. LÄS MER

  5. 4. Predicting Health and Living Standards of India using Deep Learning

    Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Sarath Mookola Raveendran; [2022-10-14]
    Nyckelord :IWI index; Deep CNN; poverty; ResNet-18; Deep Learning; multispectral images; nightlight images; India; health and living standards;

    Sammanfattning : Poverty eradication is an inexorable process in human growth [21], with poverty estimation being the first and most important stage. Identifying strategies for poverty reduction programs and distributing resources appropriately requires determining the poverty levels of distinct places throughout the world. LÄS MER

  7. 5. 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