BROWSE

Author Profile

Browsing by Author Francisco, Argüello

Jump to: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
or enter first few letters:  
Showing results [1 - 1] / 1
    • A hybrid CUDA, OpenMP, and MPI parallel TCA-based domain adaptation for classification of very high-resolution remote sensing images
    Authors: Alberto S., Garea; Dora B., Heras; Francisco, Argüello;  Advisor: -;  Co-Author: - (2023)

    Domain Adaptation (DA) is a technique that aims at extracting information from a labeled remote sensing image to allow classifying a different image obtained by the same sensor but at a different geographical location. This is a very complex problem from the computational point of view, specially due to the very high-resolution of multispectral images. TCANet is a deep learning neural network for DA classification problems that has been proven as very accurate for solving them. TCANet consists of several stages based on the application of convolutional filters obtained through Transfer Component Analysis (TCA) computed over the input images.