Guided Ridge Regression-Based Polarimetric-Spatial Feature Extraction for Classification of Polarimetric SAR Images

Guided Ridge Regression-Based Polarimetric-Spatial Feature Extraction for Classification of Polarimetric SAR Images

Maryam Imani

Abstract

Synthetic aperture radar (SAR) acquires high resolution images containing rich spatial information. The Polarimetric SAR (PolSAR) images are a good source of polarimetric and spatial information appropriate for land cover classification. Two PolSAR image classification methods are introduced in this work: ridge regression-based polarimetric-spatial (RRPS) and the guided RRPS. The RRPS feature extraction method generates polarimetric-spatial features with minimum overlapping and redundant information. To this end, each polarimetric-spatial channel of the PolSAR data is represented through a ridge regression model using the farthest neighbors of that channel. The weights of the regression model compose the projection matrix for dimensionality reduction. The guided RRPS method uses the weights of the guided filter to revise the probability maps corresponding to the initial classification map. The proposed RRPS and guided RRPS methods show high performance for PolSAR image classification in small sample size situations. 

Keywords

Ridge regression, Polarimetric SAR, feature space projection, classification, guided filter

References

  • [1] A. Liu, F. Wang, H. Xu and L. Li, "N-SAR: A New Multichannel Multimode Polarimetric Airborne SAR," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 9, pp. 3155-3166, Sept. 2018.
  • [2] M. Ahishali, S. Kiranyaz, T. Ince and M. Gabbouj, "Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks," 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia, pp. 73-76, 2020.
  • [3] P. A. A. Penna and N. D. A. Mascarenhas, "SAR Speckle Nonlocal Filtering With Statistical Modeling of Haar Wavelet Coefficients and Stochastic Distances," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 7194-7208, Sept. 2019.
  • [4] S. Wang, J. Pei, K. Liu, S. Zhang and B. Chen, "Unsupervised classification of POLSAR data based on the polarimetric decomposition and the co-polarization ratio," 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, pp. 424-427, 2011.
  • [5] H. Maurya and R. K. Panigrahi, "PolSAR image classification using generalized scattering models," 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL), Singapore, pp. 408-412, 2017.
  • [6] S. T. Tu, J. Y. Chen, W. Yang and H. Sun, "Laplacian Eigenmaps-Based Polarimetric Dimensionality Reduction for SAR Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 1, pp. 170-179, Jan. 2012.
  • [7] L. Zhang, L. Sun and W. M. Moon, "Polarimetric SAR image classification based on contextual sparse representation," 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, pp. 1837-1840, 2015.
  • [8] H. Bi, L. Xu, X. Cao, Y. Xue and Z. Xu, "Polarimetric SAR Image Semantic Segmentation With 3D Discrete Wavelet Transform and Markov Random Field," in IEEE Transactions on Image Processing, vol. 29, pp. 6601-6614, 2020.
  • [9] A. Masjedi, M. J. Valadan Zoej and Y. Maghsoudi, "Classification of Polarimetric SAR Images Based on Modeling Contextual Information and Using Texture Features," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 2, pp. 932-943, Feb. 2016.
  • [10] B. Zou, X. Xu and L. Zhang, "Object-Based Classification of PolSAR Images Based on Spatial and Semantic Features," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 609-619, 2020.
  • [11] A. Tombak, İ. Türkmenli, E. Aptoula and K. Kayabol, "Pixel-Based Classification of SAR Images Using Feature Attribute Profiles," in IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 4, pp. 564-567, April 2019.
  • [12] P. Zhang, B. Li, X. Tan, Y. Jiang, M. Li and Y. Wu, "Hybrid Conditional Random Fields Based on Complex-valued 3D CNN for PolSAR Image Classification," 2020 IEEE Radar Conference (RadarConf20), 2020, pp. 1-6.
  • [13] X. Nie, R. Gao, R. Wang and D. Xiang, "Online Multiview Deep Forest for Remote Sensing Image Classification via Data Fusion," in IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 8, pp. 1456-1460, Aug. 2021.
  • [14] X. Mao, X. Xiao and Y. Lu, "PolSAR Data-Based Land Cover Classification Using Dual-Channel Watershed Region-Merging Segmentation and Bagging-ELM," in IEEE Geoscience and Remote Sensing Letters, In Press, 2021.
  • [15] R. Sharma and R. K. Panigrahi, "Texture Classification-Based NLM PolSAR Filter," in IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 8, pp. 1396-1400, Aug. 2021.
  • [16] M. Imani and H. Ghassemian, "Morphology-based structure-preserving projection for spectral–spatial feature extraction and classification of hyperspectral data," IET Image Processing, vol. 13, no. 2, pp. 270-279, Feb. 2019.
  • [17] K. Cho, S. Park, J. Cho, H. Moon and S. Han, "Automatic Urban Area Extraction From SAR Image Based on Morphological Operator," in IEEE Geoscience and Remote Sensing Letters, 2020.
  • [18] X. Kang, S. Li and J. A. Benediktsson, "Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2666-2677, May 2014.
  • [19] X. Kang, S. Li and J. A. Benediktsson, "Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 6, pp. 3742-3752, June 2014.
  • [20] M. Imani, "A Random Patches Based Edge Preserving Network for Land Cover Classification Using Polarimetric Synthetic Aperture Radar Images," International Journal of Remote Sensing, 2021.
  • [21] M. Imani and H. Ghassemian, "Ridge regression-based feature extraction for hyperspectral data," International Journal of Remote Sensing, vol. 36, no. 6, pp. 1728–1742, 2015.
  • [22] Qingfu Zhang and Yiu Wing Leung, "A class of learning algorithms for principal component analysis and minor component analysis," in IEEE Transactions on Neural Networks, vol. 11, no. 1, pp. 200-204, Jan. 2000.
  • [23] M. Imani and H. Ghassemian, "The Investigation of Sensitivity of SVM Classifier Respect to The Number of Features and The Number of Training Samples," 2nd International Conference on Sensors and Models in Photogrammetry and Remote Sensing, Tehran, Iran, pp. 209-214, 5 – 8 October 2013.
  • [24] L. Gomez, L. Alvarez, L. Mazorra and A. C. Frery, "Fully PolSAR image classification using machine learning techniques and reaction-diffusion systems," Neurocomputing, vol. 255, pp. 52-60, 2017.
  • [25] X. Nie, S. Ding, X. Huang, H. Qiao, B. Zhang and Z. -P. Jiang, "An Online Multiview Learning Algorithm for PolSAR Data Real-Time Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 1, pp. 302-320, Jan. 2019.
  • [26] Z. Zhang, H. Wang, F. Xu and Y. Jin, "Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 12, pp. 7177-7188, Dec. 2017.
  • [27] J. Cohen, “A coefficient of agreement from nominal scales,” Edu. Psychol. Meas., vol. 20, no. 1, pp. 37–46, 1960.
  • [28] G. M. Foody, “Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy,” Photogramm. Eng. Remote Sens., vol. 70, no. 5, pp. 627–633, 2004.
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