@inproceedings{727229eaba2140c39812bee901b82d8c,
title = "An unsupervised automatic change detection approach based on visual attention mechanism",
abstract = "In change detection analysis, it is important to distinguish the real change targets and pseudo change targets accurately. Supervised change detection has been regarded as the best way to reduce the effects of pseudo change information. This is because human visual system has the ability to find the real changes. By imitating human visual characteristic, visual attention mechanism can bring the improvement of accuracy and speed of unsupervised change detection. In this paper, a change detection approach based on visual attention mechanism is proposed to reduce the influence of pseudo change information. Experiments show that the proposed method significantly reduces the false alarm rate and missed alarm rate and also shows insensitive to noise.",
keywords = "Change detection, feature extraction, remote sensing image, saliency map, visual attention",
author = "Donghua Liu and Junping Zhang and Xiaochen Lu",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 ; Conference date: 26-07-2015 Through 31-07-2015",
year = "2015",
month = nov,
day = "10",
doi = "10.1109/IGARSS.2015.7326458",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3045--3048",
booktitle = "2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings",
address = "美国",
}