TY - GEN
T1 - 2D-SSA based multiscale feature fusion for feature extraction and data classification in hyperspectral imagery
AU - Fu, Hang
AU - Sun, Genyun
AU - Ren, Jinchang
AU - Zabalza, Jaime
AU - Zhang, Aizhu
AU - Yao, Yanjuan
N1 - © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - Singular spectrum analysis (SSA) and its 2-D variation (2D-SSA) have been successfully applied for effective feature extraction in hyperspectral imaging (HSI). However, they both cannot effectively use the spectral-spatial information, leading to a limited accuracy in classification. To tackle this problem, a novel 2D-SSA based multiscale feature fusion method, combining with segmented principal component analysis (SPCA), is proposed in this paper. The SPCA method is used for dimension reduction and spectral feature extraction, while multiscale 2D-SSA can extract abundant spatial features at different scales. In addition, a postprocessing via SPCA is applied on fused features to enhance the spectral discriminability. Experiments on two widely used datasets show that the proposed method outperforms two conventional SSA methods and other spectral-spatial classification methods in terms of the classification accuracy and computational cost.
AB - Singular spectrum analysis (SSA) and its 2-D variation (2D-SSA) have been successfully applied for effective feature extraction in hyperspectral imaging (HSI). However, they both cannot effectively use the spectral-spatial information, leading to a limited accuracy in classification. To tackle this problem, a novel 2D-SSA based multiscale feature fusion method, combining with segmented principal component analysis (SPCA), is proposed in this paper. The SPCA method is used for dimension reduction and spectral feature extraction, while multiscale 2D-SSA can extract abundant spatial features at different scales. In addition, a postprocessing via SPCA is applied on fused features to enhance the spectral discriminability. Experiments on two widely used datasets show that the proposed method outperforms two conventional SSA methods and other spectral-spatial classification methods in terms of the classification accuracy and computational cost.
KW - segmented principal component analysis (SPCA)
KW - hyperspectral imagery (HSI)
KW - multiscale 2D-SSA
KW - feature extraction
KW - data classification
U2 - 10.1109/IGARSS39084.2020.9323776
DO - 10.1109/IGARSS39084.2020.9323776
M3 - Conference contribution book
SN - 9781728163741
SP - 76
EP - 79
BT - IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
PB - IEEE
CY - Piscataway, N.J.
ER -