Exponential Weighted Random Forest for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

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Exponential Weighted Random Forest for Hyperspectral Image Classification

Published:2019-07 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Jain Vikas,Phophalia Ashish

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8891871/8897702/08897862.pdf?arnumber=8897862

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements;Journal of Agriculture and Food Research;2024-12

2. Advanced graph deep learning for High-dimensional image analysis: challenges and opportunities;2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP);2024-07-11

3. An extensive review of hyperspectral image classification and prediction: techniques and challenges;Multimedia Tools and Applications;2024-03-09

4. Hyperspectral Image Classification Based on Spectral–Spatial Attention Tensor Network;IEEE Geoscience and Remote Sensing Letters;2024

5. Deep-Growing Neural Network With Manifold Constraints for Hyperspectral Image Classification;IEEE Transactions on Neural Networks and Learning Systems;2024

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