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BNResNet: Batch Normalization-Inspired Deep Bottleneck Residual Architecture for Aerial Scene Recognition in Low-Contrast Remote Sensing Images

Remote sensing (RS) images are evolving daily for their applications in surveillance, planned urbanization, law enforcement, climate change detection, agriculture, and monitoring catastrophes. Artificial intelligence techniques in this application heavily depend on the quality of RS images. The low-...

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Principais autores: Saddaf Rubab, Muhammad Sami Ullah, Muhammad Attique Khan, Mohammad Shabaz, Aliya Aleryani, Monia Turki-Hadj Alouane, Fatimah Alhayan
Format: Artigo
Sprog:Inglês
Udgivet: IEEE 2025-01-01
Serier:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Fag:
Coastal areas
deep bottleneck
interpretation
low resolution
optimized residual block
remote sensing (RS)
Online adgang:https://ieeexplore.ieee.org/document/11108310/
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