Spatial-Spectral Classification of Hyperspectral Images Based on Extended Morphological Profiles and Guided Filter

Document Type : Image Processing-Pourreza

Authors

• behnam asghari beirami ¹
• mehdi Mokhtarzade ²

¹ K. N. Toosi university

² K. N. Toosi University of Technology

10.22067/cke.v2i2.81519

Abstract

Previous studies show that the incorporation of spatial features in the classification process of hyperspectral images (HSI) improves classification accuracy. Although different spatial-spectral methods are proposed in the literature for the classification of the HSI, they almost have a slow, complex, and parameter-dependent structure. This paper proposes, a simple, fast and efficient two-stage spatial-spectral method for the classification of the HSI based on extended morphological profiles (EMP) and the guided filter. The proposed method consists of four major stages. In the first stage, principal component analysis (PCA) is used to smooth the HSI to extract the low-dimensional informative features. In the second stage, EMP is produced from the first three PCs. Stacked feature vectors, consisting of PCs and EMP, are classified via support vector machines (SVM) in the third step. Finally, a post-processing stage based on a guided filter is applied to classified maps to further improve the classification accuracy and to refine the noisy classified pixels. Experimental results on two famous hyperspectral images named Indian Pines and Pavia University in a very small training sample size situation show that the proposed method can reach the high level of accuracies which are superior to some recent state-of-the-art methods.

Keywords

• Extended Morphological Profiles
• Guided Filter
• Support vector machine
• Principal Components Analysis
• Hyperspectral
• Classification

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References

[1]. Z. A. Andekah, M. Naderan, and G. Akbarizade, "Semi-supervised hyperspectral image classification using spatial-spectral features and superpixel-based sparse codes". Presented at IEEE Iranian Conference on Electrical Engineering (ICEE). Tehran, Iran, (2017).

[2]. Ch. Chang, "Hyperspectral data processing: algorithm design and analysis," John Wiley & Sons, (2013).

[3]. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas," EURASIP Journal on Advances in Signal Processing, pp. 1-14, (2009).

[4]. W. Li, S. Prasad, J. E. Fowler, L. M. Bruce, "Locality-preserving dimensionality reduction and classification for hyperspectral image analysis," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 4, pp. 1185-1198, (2011).

[5]. Q. Wang, Z. Meng, and X. Li, "Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 11, pp. 2077-2081, (2017).

[6]. Y. Dong, B. Du, L. Zhang, L. Zhang, "Dimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp. 2509-2524, (2017).

[7] .B. Guo, S. R. Gunn, R. I. Damper, J. D. Nelson., "Band selection for hyperspectral image classification using mutual information," IEEE Geoscience and Remote Sensing Letters, vol. 3, no. 4, pp. 522-526, (2006).

[8]. J. Jiang, J. Ma, C. Chen, Z. Wang, Z. Cai, L.Wang. (2018). SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 56(8), pp. 4581-4593.

[9]. Beirami, Behnam Asghari, and Mehdi Mokhtarzade. (2020). Band Grouping SuperPCA for Feature Extraction and Extended Morphological Profile Production from Hyperspectral Images. IEEE Geoscience and Remote Sensing Letters. DOI: 10.1109/LGRS.2019.2958833

[10]. L. Zhang, H. Su, and J. Shen, "Hyperspectral Dimensionality reduction based on multiscale superpixelwise kernel principal component analysis, Remote Sensing. vol. 11, no. 10, pp. 1219.

[11] . B.A. Beirami and M. Mokhtarzade. (2017). SVM classification of hyperspectral images using the combination of spectral bands and Moran's I features. Presented at IEEE 10th Iranian Conference on Machine Vision and Image Processing (MVIP). Isfahan, Iran.

[12]. Ayerdi, Borja, Ion Marques, and Manuel Graña. "Spatially regularized semisupervised ensembles of extreme learning machines for hyperspectral image segmentation," Neurocomputing, vol. 149, pp. 373-386, (2015).

[13]. Li, Wei, and Qian Du, "A survey on representation-based classification and detection in hyperspectral remote sensing imagery," Pattern Recognition Letters, vol. 83, pp. 115-123, (2016).

[14]. F. Gao, Q. Wang, J. Dong, Q. Xu. "Spectral and spatial classification of hyperspectral images based on random multi-graphs," Remote Sensing, vol. 10, no. 8, pp. 1271, 2018. doi:10.3390/rs10081271.

[15]. W. Zhao, X. Chen, J. Chen, Y. Qu, "Sample generation with self-attention generative adversarial Adaptation Network (SaGAAN) for Hyperspectral Image Classification," Remote Sensing, vol. 12, 5, pp. 843, (2020).

[16]. A. Davari, H. C. Özkan, A. Maier, C. Riess, "Fast and efficient limited data hyperspectral remote sensing image classification via GMM-Based Synthetic Samples," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 7, pp. 2107-2120, (2019).

[17] . J. A. Benediktsson, J. A. Palmason, J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 3, pp. 480-491, (2005).

[18] . M. Pesaresi, and J. A. Benediktsson, "A new approach for the morphological segmentation of high-resolution satellite imagery," IEEE transactions on Geoscience and Remote Sensing, vol. 39, no. 2, pp. 309-320, (2001).

[19] . K. Tan, and P. Du. "Classification of hyperspectral image based on morphological profiles and multi-kernel SVM," Presented at IEEE 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Reykjavik, Iceland, (2010).

[20]. B. Kumar, and O. Dikshit, "Hyperspectral image classification based on morphological profiles and decision fusion. International Journal of Remote Sensing. vol. 38, no. 20, pp. 5830-5854, (2017).

[21] . K. He, J. Sun, and X. Tang, "Guided image filtering. IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, (2001).

[22]. H. Ma, W. Feng, X. Cao, L. Wang “Classification of hyperspectral data based on guided filtering and random forest,” in Proc. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, pp. 821-824.

[23] . Kang, X., S. Li, and J.A. Benediktsson (2014). Spectral–spatial hyperspectral image classification with edge-preserving filtering. IEEE Transactions on Geoscience and Remote Sensing. 52(5), pp. 2666-2677.

[24]. B. Pan, Z. Shi, and X. Xu. "Hierarchical guidance filtering-based ensemble classification for hyperspectral images," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 4177-4189, (2019).

[25] . X. Cao, B. Ji, Y. Ji, L. Wang, L. Jiao, "Hyperspectral image classification based on filtering: a comparative study,". Journal of Applied Remote Sensing, vol. 11, no. 3, pp. 035007-1 -035007-19, 2017. doi: 10.1117/1. JRS.11.035007.

[26]. J. Jiang, C. Chen, Y. Yu, X. Jiang, J. Ma, "Spatial-aware collaborative representation for hyperspectral remote sensing image classification," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 3, pp. 404-408, (2017).

[27]. Y. Xu, B. Du, F. Zhang, L. Zhang, "Hyperspectral image classification via a random patches network," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 142, pp. 344-357, (2018).

[28]. F. Feng, S. Wang, C. Wang, J. Zhang, "Learning deep hierarchical spatial–spectral features for hyperspectral image classification based on residual 3D-2D CNN. Sensors, vol. 19, no. 23, pp. 5276, (2019).

[29]. F. Mirzapour, and H. Ghassemian. "Moment-based feature extraction from high spatial resolution hyperspectral images," International Journal of Remote Sensing, vol. 37, no. 6, pp. 1349-1361, (2016.

[30] . R.C. Gonzalez, “Digital image processing,” Prentice hall, (2016).

[31] . M. Fauvel, J. A. Benediktsson, J. Chanussot, J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles," IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 11, pp. 3804-3814, (2008).

[32]. P. Soille, “Morphological image analysis: principles and applications,” Springer Science & Business Media, (2013).

[33] . C. C. Chang, and C. J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 27:1-27:27, (2011).