Publication Cover
Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 49, 2023 - Issue 1
365
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Hyperspectral Image Classification Based on the Gabor Feature with Correlation Information

Classification d’images hyperspectrales basée sur le paramètre de Gabor avec des informations de corrélation

, &
Article: 2246158 | Received 30 Nov 2022, Accepted 08 Jun 2023, Published online: 25 Aug 2023

References

  • Bai, J., Ding, B., Xiao, Z., Jiao, L., Chen, H., and Regan, A.C. 2022. “Hyperspectral image classification based on deep attention graph convolutional network.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–16. doi:10.1109/TGRS.2021.3066485.
  • Bau, T.C., Sarkar, S., and Healey, G. 2010. “Hyperspectral region classification using a three-dimensional Gabor filterbank.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 48(No. 9): pp. 3457–3464. doi:10.1109/TGRS.2010.2046494.
  • Bhatti, U.A., Yu, Z., Chanussot, J., Zeeshan, Z., Yuan, L., Luo, W., Nawaz, S.A., Bhatti, M.A., Ain, Q.U., and Mehmood, A. 2022. “Local similarity-based spatial-spectral fusion hyperspectral image classification with deep CNN and Gabor filtering.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–15. doi:10.1109/TGRS.2021.3090410.
  • Cai, W., Liu, B., Wei, Z., Li, M., and Kan, J. 2021. “TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification.” Multimedia Tools and Applications, Vol. 80(No. 7): pp. 11291–11312. doi:10.1007/s11042-020-10188-x.
  • Cao, L., He, J., Gao, L., Zhong, Y., Hu, X., and Li, Z. 2022. “LWIR hyperspectral image classification based on a temperature-emissivity residual network and conditional random field model.” International Journal of Remote Sensing, Vol. 43(No. 10): pp. 3744–3768. doi:10.1080/01431161.2022.2105667.
  • Cao, X., Wang, D., Wang, X., Zhao, J., and Jiao, L. 2020. “Hyperspectral imagery classification with cascaded support vector machines and multi-scale superpixel segmentation.” International Journal of Remote Sensing, Vol. 41(No. 12): pp. 4530–4550. doi:10.1080/01431161.2020.1723172.
  • Fatemighomi, H.S., Golalizadeh, M., and Amani, M. 2022. “Object-based hyperspectral image classification using a new latent block model based on hidden Markov random fields.” Pattern Analysis and Applications, Vol. 25(No. 2): pp. 467–481. doi:10.1007/s10044-021-01050-3.
  • Gastal, E.S., and Oliveira, M.M. 2012. “Adaptive manifolds for real-time high-dimensional filtering.” ACM Transactions on Graphics, Vol. 31(No. 4):pp. 1–13. doi:10.1145/2185520.2185529.
  • Ghassemi, M., Ghassemian, H., and Imani, M. 2021. “Hyperspectral image classification by optimizing convolutional neural networks based on information theory and 3D-Gabor filters.” International Journal of Remote Sensing, Vol. 42(No. 11): pp. 4380–4410. doi:10.1080/01431161.2021.1892854.
  • Guo, Y., Cao, H., Han, S., Sun, Y., and Bai, Y. 2018a. “Spectral–spatial hyperspectral image classification with k-nearest neighbor and guided filter.” IEEE Access, Vol. 6: pp. 18582–18591. doi:10.1109/ACCESS.2018.2820043.
  • Guo, Y., Han, S., Li, Y., Zhang, C., and Bai, Y. 2018b. “K-Nearest Neighbor combined with guided filter for hyperspectral image classification.” Procedia Computer Science, Vol. 129: pp. 159–165. doi:10.1016/j.procs.2018.03.066.
  • Guo, Z., Zhang, M., Jia, W., Zhang, J., and Li, W. 2022. “Dual-concentrated network with morphological features for tree species classification using hyperspectral image.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15: pp. 7013–7024. doi:10.1109/JSTARS.2022.3199618.
  • Haghighat, M., Zonouz, S., and Abdel-Mottaleb, M. 2015. “CloudID: Trustworthy cloud-based and cross-enterprise biometric identification.” Expert Systems with Applications, Vol. 42(No. 21): pp. 7905–7916. doi:10.1016/j.eswa.2015.06.025.
  • Hao, S., Liu, R., Lin, X., Li, C., Guo, H., Ye, Z., and Wang, C. 2022. “Configuration design and gait planning of a six-bar tensegrity robot.” Applied Sciences, Vol. 12(No. 22): pp. 11845. doi:10.3390/app122211845.
  • He, K., Sun, J.., and Tang, X. 2012. “Guided image filtering.” IEEE transactions on pattern analysis and machine intelligence, Vol. 36(No. 6): pp. 1397–1409. doi:10.1109/TPAMI.2012.213.
  • He, L., Li, J., Plaza, A., and Li, Y. 2017. “Discriminative low-rank Gabor filtering for spectral–spatial hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 55(No. 3): pp. 1381–1395. doi:10.1109/TGRS.2016.2623742.
  • Hu, Q., Xu, W., Liu, X., Cai, Z., and Cai, J. 2022. “Hyperspectral image classification based on bilateral filter with multispatial domain.” IEEE Geoscience and Remote Sensing Letters, Vol. 19: pp. 1–5. doi:10.1109/LGRS.2021.3058182.
  • Huang, K.K., Ren, C.X., Liu, H., Lai, Z.R., Yu, Y.F., and Dai, D.Q. 2022. “Hyperspectral image classification via discriminant Gabor ensemble filter.” IEEE Transactions on Cybernetics, Vol. 52(No. 8): pp. 8352–8365. doi:10.1109/TCYB.2021.3051141.
  • Imani, M., and Ghassemian, H. 2016. GLCM, Gabor, and morphology profiles fusion for hyperspectral image classification. 2016 24th Iranian Conference on Electrical Engineering (ICEE), pp. 460–465. IEEE. doi:10.1109/IranianCEE.2016.7585566.
  • Jia, S., Hu, J., Xie, Y., Shen, L., Jia, X., and Li, Q. 2016. “Gabor cube selection based multitask joint sparse representation for hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 54(No. 6): pp. 3174–3187. doi:10.1109/TGRS.2015.2513082.
  • Jia, S., Shen, L., Zhu, J., and Li, Q. 2018. “A 3-D Gabor phase-based coding and matching framework for hyperspectral imagery classification.” IEEE Transactions on Cybernetics, Vol. 48(No. 4): pp. 1176–1188. doi:10.1109/TCYB.2017.2682846.
  • Kang, X., Li, C., Li, S., and Lin, H. 2018. “Classification of hyperspectral images by Gabor filtering based deep network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11(No. 4): pp. 1166–1178. doi:10.1109/JSTARS.2017.2767185.
  • Kang, X., Li, S., and Benediktsson, J.A. 2014a. “Feature extraction of hyperspectral images with image fusion and recursive filtering.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52(No. 6): pp. 3742–3752. doi:10.1109/TGRS.2013.2275613.
  • Kang, X., Li, S., and Benediktsson, J.A. 2014b. “Spectral–spatial hyperspectral image classification with edge-preserving filtering.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52(No. 5): pp. 2666–2677. doi:10.1109/TGRS.2013.2264508.
  • Kang, X., Xiang, X., Li, S., and Benediktsson, J.A. 2017. “PCA-based edge-preserving features for hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 55(No. 12): pp. 7140–7151. doi:10.1109/TGRS.2017.2743102.
  • Kotwal, K., and Chaudhuri, S. 2010. “Visualization of hyperspectral images using bilateral filtering.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 48(No. 5): pp. 2308–2316. doi:10.1109/TGRS.2009.2037950.
  • Lei, R., Zhang, C., Liu, W., Zhang, L., Zhang, X., Yang, Y., Huang, J., Li, Z., and Zhou, Z. 2021. “Hyperspectral remote sensing image classification using deep convolutional capsule network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14: pp. 8297–8315. doi:10.1109/JSTARS.2021.3101511.
  • Li, W., and Du, Q. 2014. “Gabor-filtering-based nearest regularized subspace for hyperspectral image classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7(No. 4): pp. 1012–1022. doi:10.1109/JSTARS.2013.2295313.
  • Liao, J., and., and Wang, L. 2020. “Adaptive hyperspectral image classification based on the fusion of manifolds filter and spatial correlation features.” IEEE Access, Vol. 8: pp. 90390–90409. doi:10.1109/ACCESS.2020.2993864.
  • Liao, J., and., and Wang, L. 2020. “Multiple spatial features extraction and fusion for hyperspectral images classification.” Canadian Journal of Remote Sensing, Vol. 46(No. 2): pp. 193–213. doi:10.1080/07038992.2020.1768837.
  • Liao, J., Wang, L., Hao, S., and Zhao, G. 2019a. “Hyperspectral image classification based on fusion of guided filter and domain transform interpolated convolution filter.” Canadian Journal of Remote Sensing, Vol. 44(No. 5): pp. 476–490. doi:10.1080/07038992.2018.1546571.
  • Liao, J., Wang, L., Zhao, G., and Hao, S. 2019b. “Hyperspectral image classification based on bilateral filter with linear spatial correlation information.” International Journal of Remote Sensing, Vol. 40(No. 17): pp. 6861–6883. doi:10.1080/01431161.2019.1597301.
  • Liu, R., Cai, W., Li, G., Ning, X., and Jiang, Y. 2022. “Hybrid dilated convolution guided feature filtering and enhancement strategy for hyperspectral image classification.” IEEE Geoscience and Remote Sensing Letters, Vol. 19: pp. 1–5. doi:10.1109/LGRS.2021.3100407.
  • Luo, F., Zou, Z., Liu, J., and Lin, Z. 2022. “Dimensionality reduction and classification of hyperspectral image via multistructure unified discriminative embedding.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 60: pp. 1–16. doi:10.1109/TGRS.2021.3128764.
  • Pan, H., Liu, M., Ge, H., and Chen, S. 2022. “Semi-supervised spatial–spectral classification for hyperspectral image based on three-dimensional Gabor and co-selection self-training.” Journal of Applied Remote Sensing, Vol. 16(No. 2): pp. 028501–028501. doi:10.1117/1.JRS.16.028501.
  • Rajadell, O., Garcia-Sevilla, P., and Pla, F. 2013. “Spectral–spatial pixel characterization using Gabor filters for hyperspectral image classification.” IEEE Geoscience and Remote Sensing Letters, Vol. 10(No. 4): pp. 860–864. doi:10.1109/LGRS.2012.2226426.
  • Sellami, A., and Tabbone, S. 2022. “Deep neural networks-based relevant latent representation learning for hyperspectral image classification.” Pattern Recognition, Vol. 121: pp. 108224. doi:10.1016/j.patcog.2021.108224.
  • Shambulinga, M., and Sadashivappa, G. 2019. “Hyperspectral image classification using support vector machine with guided image filter.” International Journal of Advanced Computer Science and Applications, Vol. 10(No. 10) pp. 271–276. doi:10.14569/IJACSA.2019.0101038.
  • Shen, L., and Bai, L. 2006. “MutualBoost learning for selecting Gabor features for face recognition.” Pattern Recognition Letters, Vol. 27(No. 15): pp. 1758–1767. doi:10.1016/j.patrec.2006.02.005.
  • Shen, L., and Jia, S. 2011. “Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 49(No. 12): pp. 5039–5046.
  • Sun, H., Zheng, X., and Lu, X. 2021. “A supervised segmentation network for hyperspectral image classification.” IEEE Transactions on Image Processing, Vol. 30: pp. 2810–2825. doi:10.1109/TIP.2021.3055613.
  • Tan, X., Gao, K., Liu, B., Fu, Y., and Kang, L. 2021. “Deep global-local transformer network combined with extended morphological profiles for hyperspectral image classification.” Journal of Applied Remote Sensing, Vol. 15(No. 03): pp. 038509–038509. doi:10.1117/1.JRS.15.038509.
  • Vaddi, R., and Manoharan, P. 2020. “CNN based hyperspectral image classification using unsupervised band selection and structure-preserving spatial features.” Infrared Physics & Technology, Vol. 110: pp. 103457. doi:10.1016/j.infrared.2020.103457.
  • Wang, L., Hao, S., Wang, Q., and Wang, Y. 2014. “Semi-supervised classification for hyperspectral imagery based on spatial-spectral label propagation.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 97: pp. 123–137. doi:10.1016/j.isprsjprs.2014.08.016.
  • Wang, L., Hao, S., Wang, Y., Lin, Y., and Wang, Q. 2014. “Spatial–spectral information-based semisupervised classification algorithm for hyperspectral imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7(No. 8): pp. 3577–3585. doi:10.1109/JSTARS.2014.2333233.
  • Xia, J., Bombrun, L., Adalı, T., Berthoumieu, Y., and Germain, C. 2016. “Spectral–spatial classification of hyperspectral images using ICA and edge-preserving filter via an ensemble strategy.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 54(No. 8): pp. 4971–4982. doi:10.1109/TGRS.2016.2553842.
  • Xiao, G., Wei, Y., Yao, H., Deng, W., Xu, J., and Pan, D. 2022. “Hierarchical broad learning system for hyperspectral image classification.” IET Image Processing, Vol. 16(No. 2): pp. 554–566. doi:10.1049/ipr2.12371.
  • Ye, Z., Bai, L., and Nian, Y.J. 2016. “Hyperspectral image classification algorithm based on Gabor feature and locality-preserving dimensionality reduction.” Acta Optica Sinica, Vol. 36(No. 10): pp. 1028003.
  • Zhan, K., Wang, H., Huang, H., and Xie, Y. 2016. “Large margin distribution machine for hyperspectral image classification.” Journal of Electronic Imaging, Vol. 25(No. 6): pp. 063024. doi:10.1117/1.JEI.25.6.063024.
  • Zhang, T., and Zhou, Z. H. 2014. Large margin distribution machine. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 313–322. ACM.