Neural Network Autoencoder for Change Detection in Satellite Image Time Series

被引：0
|
作者：
Kalinicheva, Ekaterina [1 ]
Sublime, Jeremie [1 ]
Trocan, Maria [1 ]
机构：
[1] ISEP, LISITE, Issy Les Moulineaux, France
来源：
2018 25TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS) | 2018年
关键词：
satellite image processing; change detection; deep learning; unsupervised learning; neural networks; convolutional autoencoder with fully-connected layers; feature extraction;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper introduces a new algorithm for satellite image time series change detection. This algorithm is based on image subtraction analysis and does not directly work on raw images, but on their encoded feature representation version. The encoding is realized with neural network autoencoder. The change detection method is totally unsupervised and does not need any labeled data.
引用
收藏
页码：641 / 642
页数：2
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