@inproceedings{7f499b0ee1ae4e2ca86a05c877410342,
title = "Change detection of marine reclamation using multispectral images via patch-based recurrent neural network",
abstract = "Marine reclamation plays an increasingly important role in expanding living space, which should be monitored to ensure legitimate development. In this paper, a patch-based recurrent neural network is developed for change detection of marine reclamation. To capture spatial difference of image patches in two images, a patch-based recurrent neural network is proposed to extract features, where patches from two multispectral images are stacked as a sequence for inputting. After training the deep network, Softmax classifier is applied to detect the changed region. It is illustrated that our network can obtain the difference of two images to improve detection accuracies. Experiments on the study area of the Jinzhou Bay demonstrate that the proposed method outperforms other approaches.",
keywords = "Change detection, marine reclamation, multispectral image, recurrent neural network",
author = "Jie Geng and Jianchao Fan and Hongyu Wang and Xiaorui Ma",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8127028",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "612--615",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
}
