Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/41193
Title: Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification
Authors: Zhang, Xiangrong
Liang, Yanjie
Li, Chen
Huyan, Ning
Jiao, Licheng
Zhou, Huiyu
First Published: 11-Oct-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Geoscience and Remote Sensing Letters, 2017, 14 (11), pp. 1928-1932 (5)
Abstract: For hyperspectral image (HSI) classification, it is very important to learn effective features for the discrimination purpose. Meanwhile, the ability to combine spectral and spatial information together in a deep level is also important for feature learning. In this letter, we propose an unsupervised feature learning method for HSI classification, which is based on recursive autoencoders (RAE) network. RAE utilizes the spatial and spectral information and produces high-level features from the original data. It learns features from the neighborhood of the investigated pixel to represent the whole local homogeneous area of the image. In addition, to obtain more accurate representation of the investigated pixel, a weighting scheme is adopted based on the neighboring pixels, where the weights are determined by the spectral similarity between the neighboring pixels and the investigated pixel. The effectiveness of our method is evaluated by the experiments on two hyperspectral data sets, and the results show that our proposed method has a better performance.
DOI Link: 10.1109/LGRS.2017.2737823
ISSN: 1545-598X
eISSN: 1558-0571
Links: http://ieeexplore.ieee.org/document/8065033/
http://hdl.handle.net/2381/41193
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2017, IEEE. Deposited with reference to the publisher’s open access archiving policy.
Appears in Collections:Published Articles, Dept. of Computer Science

Files in This Item:
File Description SizeFormat 
Manuscript-V2%2528final%2529.pdfPost-review (final submitted author manuscript)1.04 MBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.