Year: 2025
Type: article
Abstract: - Satellite remote sensing imagery is crucial in monitoring and evaluating urban and rural area changes. The conventional machine learning techniques applied to analyze such images tend to have limitations, such as high computational costs and the requirement of a large amount of labelled data. Deep learning offers a strong alternative, with the ability to extract features automatically and identify intricate patterns from large datasets. Convolutional Neural Networks (CNNs), including U-Net, have gained general acceptance for alleviating these shortcomings. The balanced encoderdecoder structure of U-Net architecture and skip connections make it well-suited to semantic segmentation as well as detecting changes in remote sensing images. The use of residual connections is helpful in the preservation of key information during the training process and improves model performance.
Authors: Dr. D. V. Lalitha Parameswari, Konga Mamatha, Andamdas Tejeshwini, Ganjikunta Sai Vyshnavi, Cheekati Cheekati Veena
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411494830