• Laser & Optoelectronics Progress
  • Vol. 54, Issue 8, 81002 (2017)
Liao Jianshang1、* and Wang Liguo2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    DOI: 10.3788/lop54.081002 Cite this Article Set citation alerts
    Liao Jianshang, Wang Liguo. Hyperspectral Image Classification Method Based on Fusion with Two Kinds of Spatial Information[J]. Laser & Optoelectronics Progress, 2017, 54(8): 81002 Copy Citation Text show less
    References

    [1] Tong Qingxi, Zhang Bing, Zhang Lifu. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 2016, 20(5): 689-707.

    [2] Fan Liheng, Lü Junwei, Deng Jiangsheng. Classification of hyperspectral remote sensing images based on bands grouping and classification ensembles[J]. Acta Optica Sinica, 2014, 34(9): 0910002.

    [3] Bao R, Xia J S, Dalla Mura M, et al. Combining morphological attribute profiles via an ensemble method for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 359-363.

    [4] Wang Jianing. Hyperspectral image classification based on joint sparse representation and morphological feature extraction[J]. Laser & Optoelectronics Progress, 2016, 53(8): 082801.

    [5] Chen P, Nelson J D B, Tourneret J Y. Toward a sparse Bayesian Markov random field approach to hyperspectral unmixing and classification[J]. IEEE Transactions on Image Processing, 2017, 26(1): 426-438.

    [6] Yu H Y, Gao L R, Li J, et al. Spectral-spatial classification based on subspace support vector machine and Markov random field[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016: 2783-2786.

    [7] Zhang Z, Pasolli E, Crawford M M, et al. An active learning framework for hyperspectral image classification using hierarchical segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9(2): 640-654.

    [8] Chen J K, Xia J S, Du P J, et al. Combining rotation forest and multiscale segmentation for the classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9(9): 4060-4072.

    [9] Shen L L, Bai L. MutualBoost learning for selecting Gabor features for face recognition[J]. Pattern Recognition Letters, 2006, 27(15): 1758-1767.

    [10] Ye Zhen, Bai Lin, Nian Yongjian. Hyperspectral image classification algorithm based on Gabor feature and locality-preserving dimensionality reduction[J]. Acta Optica Sinica, 2016, 36(10): 1028003.

    [11] Li W, Du Q. Gabor-filtering-based nearest regularized subspace for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(4): 1012-1022.

    [12] Wang L G, Hao S Y, Wang Y, et al. Spatial-spectral information-based semisupervised classification algorithm for hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(8): 3577-3585.

    [13] Zhu Z X, Jia S, He S, et al. Three-dimensional Gabor feature extraction for hyperspectral imagery classification using a memetic framework[J]. Information Sciences, 2015, 298: 274-287.

    [14] Jia S, Hu J, Xie Y, et al. Gabor cube selection based multitask joint sparse representation for hyperspectral image classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(6): 3174-3187.

    [15] Tomasi C, Manduchi R. Bilateral filtering for gray and color images[C]. IEEE International Conference on Computer Vision (ICCV), 1998: 839-846.

    [16] Kotwal K, Chaudhuri S. Visualization of hyperspectral images using bilateral filtering[J]. IEEE Transactions on Geoscience & Remote Sensing, 2010, 48(5): 2308-2316.

    [17] Sahadevan A S, Routray A, Das B S, et al. Hyperspectral image preprocessing with bilateral filter for improving the classification accuracy of support vector machines[J]. Journal of Applied Remote Sensing, 2016, 10(2): 025004.

    [18] Shen Y, Xu J, Li H, et al. ELM-based spectral-spatial classification of hyperspectral images using bilateral filtering information on spectral band-subsets[C]. IEEE International Geoscience and Remote Sensing Symposium, 2016: 497-500.

    [19] Wang Y, Song H W, Zhang Y. Spectral-spatial classification of hyperspectral images using joint bilateral filter and graph cut based model[J]. Remote Sensing, 2016, 8(9): 748.

    [20] Kang X D, Li S T, Benediktsson J A. Spectral-spatial hyperspectral image classification with edge-preserving filtering[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(5): 2666-2677.

    [21] Gastal E S L, Oliveira M M. Domain transform for edge-aware image and video processing[J]. ACM Transactions on Graphics (TOG), 2011, 30(4): 69.

    [22] Kang X D, Li S T, Benediktsson J A. Feature extraction of hyperspectral images with image fusion and recursive filtering[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(6): 3742-3752.

    [23] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2005, 2: 60-65.

    [24] Darbon J, Cunha A, Chan T F, et al. Fast nonlocal filtering applied to electron cryomicroscopy[C]. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008: 1331-1334.

    [25] He K, Sun J, Tang X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(6): 1397-1409.

    [26] Cai S, Du Q X Q, Moorhead R, et al. Noise-adjusted principle component analysis for hyperspectral remotely sensed imagery visualization[C]. IEEE Visualization Conference, 2005: 105.

    [27] Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience & Remote Sensing, 2004, 42(8): 1778-1790.

    Liao Jianshang, Wang Liguo. Hyperspectral Image Classification Method Based on Fusion with Two Kinds of Spatial Information[J]. Laser & Optoelectronics Progress, 2017, 54(8): 81002
    Download Citation