Multistream Convolutional Neural Network Fusion for Pixel-wise Classification of Peatland-Reference-Cited by-同舟云学术

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Multistream Convolutional Neural Network Fusion for Pixel-wise Classification of Peatland

Published:2023-06-28 Issue: Volume: Page:
ISSN:
Container-title:2023 26th International Conference on Information Fusion (FUSION)
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Author:

Farahnakian Fahimeh¹,Zelioli Luca¹,Pitkänen Timo²,Pohjankukka Jonne¹,Middleton Maarit³,Tuominen Sakari²,Nevalainen Paavo¹,Heikkonen Jukka¹

Affiliation:

1. University of Turku,Department of Computing,Finland

2. Natural Resources Institute Finland (Luke)

3. Geological Survey of Finland (GTK)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10223725/10224076/10224183.pdf?arnumber=10224183

Reference36 articles.

1. A Comparative Study of Deep Learning-based RGB-depth Fusion Methods for Object Detection

2. Distribution-balanced stratified cross-validation for accuracy estimation;zeng;Journal of Experimental & Theoretical Artificial Intelligence,2001

3. Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks

4. Comparison of algorithms that select features for pattern classifiers

5. Fusing LiDAR and Color Imagery for Object Detection using Convolutional Neural Networks

Cited by 1 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. CNN-based Boreal Peatland Fertility Classification from Sentinel-1 and Sentinel-2 Imagery;2023 IEEE International Symposium on Robotic and Sensors Environments (ROSE);2023-11-06

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