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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Autori principali:	Saddaf Rubab, Muhammad Sami Ullah, Muhammad Attique Khan, Mohammad Shabaz, Aliya Aleryani, Monia Turki-Hadj Alouane, Fatimah Alhayan
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2025-01-01
Serie:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Soggetti:	Coastal areas deep bottleneck interpretation low resolution optimized residual block remote sensing (RS)
Accesso online:	https://ieeexplore.ieee.org/document/11108310/
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