scholarly journals Consensual and Hierarchical Classification of Remotely Sensed Multispectral Images

2006 ◽  
Author(s):  
J. Lee ◽  
O. Ersoy
Keyword(s):  
Hierarchical Classification ◽  
Remotely Sensed ◽  
Multispectral Images

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals Active Learning: Any Value for Classification of Remotely Sensed Data?

2013 ◽  
Vol 101 (3) ◽  
pp. 593-608 ◽  
Author(s):  
Melba M. Crawford ◽  
Devis Tuia ◽  
Hsiuhan Lexie Yang
Keyword(s):  
Active Learning ◽  
Remotely Sensed ◽  
Remotely Sensed Data

scholarly journals A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 11204-11224 ◽  
Author(s):  
Atena Fekr ◽  
Majid Janidarmian ◽  
Katarzyna Radecka ◽  
Zeljko Zilic
Keyword(s):  
Respiration Rate ◽  
Hierarchical Classification ◽  
Rate Measurement ◽  
Medical Cloud ◽  
Respiration Rate Measurement
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