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-...

詳細記述

書誌詳細

出版年:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
主要な著者:	Saddaf Rubab, Muhammad Sami Ullah, Muhammad Attique Khan, Mohammad Shabaz, Aliya Aleryani, Monia Turki-Hadj Alouane, Fatimah Alhayan
フォーマット:	論文
言語:	英語
出版事項:	IEEE 2025-01-01
主題:	Coastal areas deep bottleneck interpretation low resolution optimized residual block remote sensing (RS)
オンライン･アクセス:	https://ieeexplore.ieee.org/document/11108310/