Paper Info
Title
A Novel Band Selection and Spatial Noise Reduction Method for Hyperspectral Image Classification
Abstract
As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral-spatial features of HSI. We proposed a new neighborhood grouping normalized matched filter (NGNMF) for BS, which can reduce the data dimension while preserving the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intraclass variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral-spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods.
Year
DOI
Venue
2022
10.1109/TGRS.2022.3189015
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Feature extraction, Data mining, Redundancy, Support vector machines, Sun, Matched filters, Hyperspectral imaging, Band selection (BS), dimensionality reduction (DR), enhanced 2-D singular spectrum analysis (E2DSSA), hyperspectral image (HSI), image classification
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Hang Fu101.01
Aizhu Zhang201.01
Genyun Sun314917.27
Jinchang Ren4114488.54
Xiuping Jia51424126.54
Zhaojie Pan600.68
Hongzhang Ma752.03