@inproceedings{2b0d1bbf796d4a8b825f389247091e96,
title = "Change detection method for hyperspectral image with sequential time series inputs",
abstract = "This paper proposes a novel change detection method of hyperspectral data based on convolutional feature extraction and long short term memory unit. To extract abstract nonlinear features of the input data, we choose deepleaming-based method, Convolutional Neural Network, to avoid the drawbacks of linear methods. Data from different period of time are processed separately. A flatten layer helps the extracted feature maps to reconstruct as a new feature vector. Experiments show the effectiveness of the proposed model compared with traditional change detection methods. The proposed method performs well in both changed samples and unchanged samples especially for hyperspectral images.",
keywords = "change detection, convolutional neural network, long short term memory unit, sequential time series",
author = "Zhang Miao and Lin Zheqi and Jia Peiyuan and Shen Yi",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8029082",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "10819--10822",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "美国",
}