TY - JOUR
T1 - Hyperspectral Image Stripe Detection and Correction Using Gabor Filters and Subspace Representation
AU - Zhang, Bing
AU - Aziz, Yashinov
AU - Wang, Zhicheng
AU - ZHUANG, Lina
AU - NG, Kwok Po
AU - Gao, Lianru
N1 - Funding information:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 42001287)
10.13039/501100001809-National Natural Science Foundation of China through the Research Fund (Grant Number: 41722108)
Hong Kong Research Grants Council General Research Fund (Grant Number: 12200317, 12300218, 12300519 and 17201020)
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2022/1
Y1 - 2022/1
N2 - Hyperspectral images (HSIs) exist in directional stripes commonly due to the failure of pushbroom acquisition. These stripes are not only vertically and horizontally oriented but also tend to be oblique. Furthermore, they can also be aperiodic and heavy. To address this problem, we propose a hyperspectral destriping algorithm, namely, GF-destriping. Taking advantage of the high sparsity and strong directionality of stripes in HSIs, Gabor filters are used to detect the stripes band by band first, and then, an advanced inpainting method, FastHyIn, is used to recover to the striped image. The numerical experiments on simulated data and real data sets show that our proposed algorithm is efficient and superior to state-of-the-art HSI destriping algorithms.
AB - Hyperspectral images (HSIs) exist in directional stripes commonly due to the failure of pushbroom acquisition. These stripes are not only vertically and horizontally oriented but also tend to be oblique. Furthermore, they can also be aperiodic and heavy. To address this problem, we propose a hyperspectral destriping algorithm, namely, GF-destriping. Taking advantage of the high sparsity and strong directionality of stripes in HSIs, Gabor filters are used to detect the stripes band by band first, and then, an advanced inpainting method, FastHyIn, is used to recover to the striped image. The numerical experiments on simulated data and real data sets show that our proposed algorithm is efficient and superior to state-of-the-art HSI destriping algorithms.
KW - Denoising
KW - Gabor filter
KW - hyperspectral image (HSI)
KW - inpainting
UR - http://www.scopus.com/inward/record.url?scp=85102699869&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2021.3061541
DO - 10.1109/LGRS.2021.3061541
M3 - Journal article
AN - SCOPUS:85102699869
SN - 1545-598X
VL - 19
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
ER -