GrI-CNN: Granulated Deep Learning Model With Interpretable Architecture for Remote Sensing Image Classification
Author:
Affiliation:
1. Center for Soft Computing Research, Indian Statistical Institute, Kolkata, West Bengal, India
2. Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore Centre, Bangalore, Karnataka, India
Funder
National Science Chair, Science and Engineering Research Board-Department of Science and Technology (SERB-DST), Govt. of India,
SERB-DST, Govt. of India
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/36/10354519/10474069.pdf?arnumber=10474069
Reference41 articles.
1. Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
2. Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
3. A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-Resolution
4. Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery
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