G2Grad-CAMRL: An Object Detection and Interpretation Model Based on Gradient-Weighted Class Activation Mapping and Reinforcement Learning in Remote Sensing Images-Reference-Cited by-同舟云学术

G2Grad-CAMRL: An Object Detection and Interpretation Model Based on Gradient-Weighted Class Activation Mapping and Reinforcement Learning in Remote Sensing Images

Published:2023 Issue: Volume:16 Page:3583-3598
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Yin Shoulin¹,Wang Liguo²^ORCID,Shafiq Muhammad³^ORCID,Teng Lin¹,Laghari Asif Ali⁴^ORCID,Khan Muhammad Faizan⁵

Affiliation:

1. College of Information and Communications Engineering, Harbin Engineering University, Harbin, China

2. College of Information and Communications Engineering, Dalian Minzu University, Dalian, China

3. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

4. Software College, Shenyang Normal University, Shenyang, China

5. Department of Information Technology, University of Haripur, Haripur, Pakistan

Funder

National Natural Science Foundation of China

Leading Talents Project of the State Ethnic Affairs Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9973430/10040626.pdf?arnumber=10040626

Reference56 articles.

1. MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images

2. Improving More Instance Segmentation and Better Object Detection in Remote Sensing Imagery Based on Cascade Mask R-CNN

3. SDGH-Net: Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression

4. Cross-Scale Feature Fusion for Object Detection in Optical Remote Sensing Images

5. PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery

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