@inproceedings{4d6f446f3a1d48f48c029c10c204f840,
title = "Fully convolutional networks for multi-temporal SAR image classification",
abstract = "Classification of crop types from multi-temporal SAR data is a complex task because of the need to extract spatial and temporal features from images affected by speckle. Previous methods applied speckle filtering and then classification in two separate processing steps. This paper introduces fully convolutional networks (FCN) for pixel-wise classification of crops from multi-temporal SAR data. It applies speckle filtering and classification in a single framework. Furthermore, it also uses dilated kernels to increase the capability to learn long distance spatial dependencies. The proposed FCN was compared with patch-based convolutional neural network (CNN) and support vector machine (SVM) classifiers. The proposed method performed better when compared with the patch-based CNN and SVM.",
keywords = "Deep learning, Fully convolutional networks, Remote Sensing, SAR, Sentinel-1",
author = "Mullissa, {Adugna G.} and Claudio Persello and Valentyn Tolpekin",
note = "Funding Information: We thank NEO BV for supplying the ground truth data. Publisher Copyright: {\textcopyright} 2018 IEEE Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/IGARSS.2018.8518780",
language = "English",
isbn = "9781538671511",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6635--6638",
booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",
}