RT Journal Article
T1 Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
A1 Acción Montes, Álvaro
A1 Argüello Pedreira, Francisco
A1 Blanco Heras, Dora
K1 Hyperspectral
K1 Classification
K1 Deep learning
K1 CNN
K1 Superpixel
K1 SLIC
K1 Data augmentation
AB Deep learning (DL) has been shown to obtain superior results for classification tasks in the field of remote sensing hyperspectral imaging. Superpixel-based techniques can be applied to DL, significantly decreasing training and prediction times, but the results are usually far from satisfactory due to overfitting. Data augmentation techniques alleviate the problem by synthetically generating new samples from an existing dataset in order to improve the generalization capabilities of the classification model. In this paper we propose a novel data augmentation framework in the context of superpixel-based DL called dual-window superpixel (DWS). With DWS, data augmentation is performed over patches centered on the superpixels obtained by the application of simple linear iterative clustering (SLIC) superpixel segmentation. DWS is based on dividing the input patches extracted from the superpixels into two regions and independently applying transformations over them. As a result, four different data augmentation techniques are proposed that can be applied to a superpixel-based CNN classification scheme. An extensive comparison in terms of classification accuracy with other data augmentation techniques from the literature using two datasets is also shown. One of the datasets consists of small hyperspectral small scenes commonly found in the literature. The other consists of large multispectral vegetation scenes of river basins. The experimental results show that the proposed approach increases the overall classification accuracy for the selected datasets. In particular, two of the data augmentation techniques introduced, namely, dual-flip and dual-rotate, obtained the best results
PB MDPI
YR 2020
FD 2020
LK http://hdl.handle.net/10347/23992
UL http://hdl.handle.net/10347/23992
LA eng
NO Acción, Á.; Argüello, F.; Heras, D.B. Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification. Appl. Sci. 2020, 10, 8833
NO The images of the Galicia dataset were obtained in partnership with the Babcock company, supported in part by the Civil Program UAVs Initiative, promoted by the Xunta de Galicia. This work was supported in part by Ministerio de Ciencia e Innovación, Government of Spain (grant numbers PID2019-104834GB-I00 and BES-2017-080920), and Consellería de Educación, Universidade e Formación Profesional (grant number ED431C 2018/19, and accreditation 2019–2022 ED431G-2019/04). All are co-funded by the European Regional Development Fund (ERDF)
DS Minerva
RD 22 abr 2026