@inproceedings{ed6421517e74488191c6ebd5adbbdc09,
title = "Convolutional Neural Network for Coastal Wetland Classification in Hyperspectral Image",
abstract = "Classifying different land cover types with hyperspectral image (HSI) is significant for restoring and protecting natural resources and maintaining ecological services in coastal wetlands. This paper proposes a multi-domain features fusion convolutional neural network (MDF-CNN) based classification method for hyperspectral images of coastal wetlands. This method adopts inter-class sparsity based discriminative least square regression (ICSDLSR) to learn a more compact and discriminative transformation, as well as fuse the high-level features of the original domain and the regression domain to obtain higher classification accuracy. Experimental results demonstrate the effectiveness of the proposed method when compared with some recent classifiers. The MDF-CNN achieved state-of-the-art performance on two latest GF-5 HSI datasets of Coastal Wetland.",
keywords = "Coastal wetlands, GF-5, convolutional neural network (CNN), feature fusion, hyperspectral imagery (HSI), least squares regression (LSR)",
author = "Chang Liu and Mengmeng Zhang and Wei Li and Weiwei Sun and Ran Tao",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 ; Conference date: 26-09-2020 Through 02-10-2020",
year = "2020",
month = sep,
day = "26",
doi = "10.1109/IGARSS39084.2020.9324383",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5104--5107",
booktitle = "2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings",
address = "United States",
}
