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    Optimizing Deep Learning Architecture for Remote Sensing Image Analysis

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    MohanSpr21.pdf (844.2Kb)
    Date
    2021-04
    Author
    Mohan, Pavithra Devy
    DeWitte, Matthew
    Rozario, Papia F.
    Gomes, Rahul
    Metadata
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    Abstract
    Deep Learning tools have become very efficient in high-resolution image analysis compared to traditional classification models. One such example is the implementation of semantic segmentation using a Convolutional Neural Network (CNN). Unlike image labeling, where images are classified into one label, we can use semantic segmentation to identify the class labels of every pixel in an image. This makes CNN an ideal tool for Land Use Land Cover (LULC) modelling. This is especially true because current land cover classification techniques require a lot of time and resources to complete. This project attempts to create a deep learning architecture for resource-constrained environments by reducing complex mathematical operations that plague the deployment of CNN. The proposed model will be trained using the Potsdam dataset and the Vaihingen dataset obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS). Using the concept of transfer learning, the trained model will then be used to compare and assess the LULC change dynamics for the lower Chippewa Valley watershed region in Wisconsin.
    Subject
    Machine learning
    Remote sensing
    Lower Chippewa River Watershed (Wis.)
    Posters
    Department of Computer Science
    Department of Geography and Anthropology
    Permanent Link
    http://digital.library.wisc.edu/1793/83285
    Type
    Presentation
    Description
    Color poster with text, images, diagrams and maps.
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