DenseNet201 based Feature Extraction for Remote Sensing Image Classification in Land Cover Monitoring-Reference-Cited by-同舟云学术

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DenseNet201 based Feature Extraction for Remote Sensing Image Classification in Land Cover Monitoring

Published:2023-12-04 Issue: Volume: Page:1-5
ISSN:
Container-title:2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC)
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Author:

Aluvala Srinivas¹,Issa Ali Ashoor²,Saranya K.³,Almoussawi Zainab abed⁴,Shakir Aboothar Mahmood⁵

Affiliation:

1. School of Computer Science & Artificial Intelligence, SR University,Warangal,Telangana,India

2. National University of Science and Technology,Dhi Qar,Iraq

3. Bannari Amman institute of technology,Department of computer science and engineering,Sathyamangalam,India

4. College of Islamic Science, Ahl Al Bayt University,karbala,Iraq

5. The Islamic university,Najaf,Iraq

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10435701/10435648/10435725.pdf?arnumber=10435725

Reference15 articles.

1. Monitoring urban growth dynamics using remote sensing and GIS techniques of Raiganj Urban Agglomeration, India

2. A Multi-Level Convolution Pyramid Semantic Fusion Framework for High-Resolution Remote Sensing Image Scene Classification and Annotation

3. Robust Object Categorization and Scene Classification over Remote Sensing Images via Features Fusion and Fully Convolutional Network

4. Deep Learning Models Performance Evaluations for Remote Sensed Image Classification

5. Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: a micro-level study

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