Multilabel Remote Sensing Image Classification with Capsule Networks

被引：2
|
作者：
Topcu, Mucahit [1 ]
Dede, Abdulkadir [1 ]
Eken, Suleyman [2 ]
Sayar, Ahmet [1 ]
机构：
[1] Kocaeli Univ, Bilgisayar Muhendisligi, Kocaeli, Turkey
[2] Kocaeli Univ, Bilisim Sistemleri Muhendisligi, Kocaeli, Turkey
来源：
2ND INTERNATIONAL CONGRESS ON HUMAN-COMPUTER INTERACTION, OPTIMIZATION AND ROBOTIC APPLICATIONS (HORA 2020) | 2020年
关键词：
capsule networks; multilabel classification; remote sensing; deep learning;
D O I：
10.1109/hora49412.2020.9152917
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
As a result of the developments in remote sensing technologies, the classification of remote sensing images according to user needs has gained great attention in recent years. Deep learning techniques are also known to increase the classification performance of remote sensing. In this study, image classification is made with capsule networks, which are deep artificial neural network model, on the multilabel datasets -Ankara Hyperspectral Image Archive and UC Merced Land Use. The performance of the classification is measured with various performance metrics.
引用
收藏
页码：316 / 318
页数：3
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