1 |
P. Du, J. Xia, W. Zhang, K. Tan, Y. Li, and S. Liu, "Multiple classifier system for remote sensing image classification: a review," Sensors 12, 4764-4792 (2012).
DOI
|
2 |
W. Zhang, F. Tian, Z. Zhao, A. Song, and L. Zhang, "Research on the technology of alternative continuous wide spectral spatial heterodyne spectrometer," Curr. Opt. Photon. 1, 295-307 (2017).
DOI
|
3 |
N. Fujimoto, Y. Takahashi, T. Moriyama, M. Shimada, H. Wakabayashi, Y. Nakatani, and S. Obayashi, "Evaluation of SPOT HRV image data received in Japan," in Proc. Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium (Vancouver, Canada, Jul. 1989), pp. 463-466.
|
4 |
B. R. Corner, R. M. Narayanan, and S. E. Reichenbach, "Noise estimation in remote sensing imagery using data masking," Int. J. Remote Sens. 24, 689-702 (2003).
DOI
|
5 |
L. Alparone, M. Selva, B. Aiazzi, S. Baronti, F. Butera, and L. Chiarantini, "Signal-dependent noise modelling and estimation of new-generation imaging spectrometers," in Proc. First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (Grenoble, France, Aug. 2009), pp. 1-4.
|
6 |
P. Fu, X. Sun, and Q. Sun, "Estimation of signal-dependent and -independent noise from hyperspectral images using a wavelet-based superpixel model," Remote Sens. Lett. 9, 906-915 (2018).
DOI
|
7 |
Z. Tang, G. Fu, J. Chen, and L. Zhang, "A unified model of noise estimation, band rejection, and de-noising for hyperspectral images," Int. J. Remote Sens. 37, 1319-1348 (2016).
DOI
|
8 |
L. Sun, B. Li, and Y. Nian, "Superpixel-based mixed noise estimation for hyperspectral images using multiple linear regression," Remote Sens. 12, 1324 (2020).
DOI
|
9 |
P. Fu, Q.-S. Sun, and Z.-X. Ji, "A spectral-spatial information based approach for the mixed noise estimation from hyperspectral remote sensing images," J. Infrared Milim. Waves 34, 236-242 (2015).
|
10 |
A. Mahmood, A. Robin, and M. Sears, "Modified residual method for estimation of signal dependent noise in hyperspectral images," in Proc. 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (Amsterdam, Netherlands, Sep. 2018), pp. 1-5.
|
11 |
P. Fu, C. Li, Y. Xia, Z. Ji, Q. Sun, W. Cai, and D. D. Feng, "Adaptive noise estimation from highly textured hyperspectral images," Appl. Opt. 53,7059-7071 (2014).
DOI
|
12 |
Y. Dian, Z. Li, and Y. Pang, "Spectral and texture features combined for forest tree species classification with airborne hyperspectral imagery," J. Indian Soc. Remote Sens. 43, 101-107 (2015).
DOI
|
13 |
H. Jie, H. Zhi, L. Jun, L. He, and Y. Wang, "3D-Gabor inspired multiview active learning for spectral-spatial hyperspectral image classification," Remote Sens. 10, 1070 (2018).
DOI
|
14 |
J. M. Haut, M. Paoletti, J. Plaza, and A. Plaza, "Cloud implementation of the K-means algorithm for hyperspectral image analysis," J. Supercomput. 73, 514-529 (2017).
DOI
|
15 |
S. Sensen, J. Zhenhong, Y. Jie, and N. Kasabov, "Image segmentation algorithm of minimum spanning tree combined with Ostu threshold method," Comput. Eng. Appl. 9, 178-183 (2019).
|
16 |
W. He, L. Zhang, L. Zhang, and H. Shen, "Hyperspectral image denoising via noise-adjusted iterative low-rank matrix approximation," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 3050-3061 (2015).
DOI
|