Local and Global Feature Adaptive Adjustment Network for Remote Sensing Image Scene Classification-Reference-Cited by-同舟云学术

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Local and Global Feature Adaptive Adjustment Network for Remote Sensing Image Scene Classification

Published:2024-04-14 Issue: Volume: Page:5580-5584
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Cao Feng¹,Liu Chang¹,Li Deyu¹,Qian Yuhua¹,Zhang Chao¹,Zhang Hu¹

Affiliation:

1. Shanxi University,School of Computer and Information Technology (School of Big Data),Taiyuan,China,030006

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10447383.pdf?arnumber=10447383

Reference22 articles.

1. Very High Resolution Multiangle Urban Classification Analysis

2. Monitoring of Urban Impervious Surfaces Using Time Series of High-Resolution Remote Sensing Images in Rapidly Urbanized Areas: A Case Study of Shenzhen

3. Object detection in optical remote sensing images: A survey and a new benchmark

4. Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

5. Comparing SIFT descriptors and gabor texture features for classification of remote sensed imagery

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1. Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies;Phyton;2024

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