پیوندهای مفید
آمار
| تعداد نشریات | 31 |
| تعداد شماره‌ها | 709 |
| تعداد مقالات | 6,688 |
| تعداد مشاهده مقاله | 9,260,141 |
| تعداد دریافت فایل اصل مقاله | 6,365,168 |
Convolutional Neural Networks with Different Dimensions for PolSAR Image Classification | ||
| Computational Sciences and Engineering | ||
| مقاله 8، دوره 2، شماره 1، تیر 2022، صفحه 69-79 اصل مقاله (982.06 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22124/cse.2022.21900.1024 | ||
| نویسنده | ||
| Maryam Imani* | ||
| Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده | ||
| Efficiency of convolutional neural networks (CNNs) with different dimensions is assessed for polarimetric synthetic aperture radar (PolSAR) image classification in this work. This article is the extended version of the paper presented in “4 International Conference on Soft Computing (CSC2021)”. A PolSAR image contains polarimetric and spatial information of materials present in the scene. So, processing of these information in one, two or three dimensions results in different outputs. Three simple architectures of CNNs with different dimensions are proposed for PolSAR image classification in this paper. A one dimensional CNN (1D CNN) is suggested for polarimetric feature extraction. A 2D CNN is presented for spatial feature extraction and a 3D CNN is introduced for polarimetric-spatial feature extraction. The performance of CNNs are compared with morphological profile of PolSAR cube when fed to the support vector machine (SVM) and random forest (RF) classifiers. The experiments are done in two cases of using 1% and 5% training samples. Superiority of 3D CNN compared to other methods is shown using different quantitative classification measures. | ||
| کلیدواژه‌ها | ||
| PolSAR؛ Classification؛ Feature extraction؛ CNN | ||
| مراجع | ||
|
[1] B. Ren, B. Hou, J. Chanussot and L. Jiao, Modified Tensor Distance-Based Multiview Spectral Embedding for PolSAR Land Cover Classification, IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 12, pp. 2095-2099, Dec. 2020. [2] O. Harant, L. Bombrun, G. Vasile, L. Ferro-Famil and M. Gay, Maximum Likelihood texture tracking in highly heterogeneous PolSAR clutter, International Geoscience and Remote Sensing Symposium, Honolulu, HI, pp. 4031-4034, 2010. [3] M. Imani, A Random Patches Based Edge Preserving Network for Land Cover Classification Using Polarimetric Synthetic Aperture Radar Images, International Journal of Remote Sensing, vol. 42, no. 13, pp. 4946–4964, 2021. [4] R. Hänsch and O. Hellwich, A Comparative Evaluation of Polarimetric Distance Measures within the Random Forest Framework for the Classification of PolSAR Images, IGARSS 2018 – International Geoscience and Remote Sensing Symposium, Valencia, pp. 8440-8443, 2018. [5] F. Shang and A. Hirose, Quaternion Neural-Network-Based PolSAR Land Classification in Poincare-Sphere-Parameter Space, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 9, pp. 5693-5703, Sept. 2014. [6] M. Ghassemi, H. Ghassemian, M. Imani, Deep Belief Networks for Feature Fusion in Hyperspectral Image Classification, International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), Bali-Indonesia, September 20-21, 2018. [7] Y. Zhou, H. Wang, F. Xu and Y. Jin, Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks, in IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 12, pp. 1935-1939, Dec. 2016. [8] W. Wu, H. Li, L. Zhang, X. Li and H. Guo, High-Resolution PolSAR Scene Classification With Pretrained Deep Convnets and Manifold Polarimetric Parameters, in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 10, pp. 6159-6168, Oct. 2018. [9] X. Liu, L. Jiao, X. Tang, Q. Sun and D. Zhang, Polarimetric Convolutional Network for PolSAR Image Classification, in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 5, pp. 3040-3054, May 2019. [10] M. Imani, Integration of the k-nearest neighbours and patch-based features for PolSAR image classification by using a two-branch residual network, Remote Sensing Letters, vol. 12, no. 11, pp. 1112–1122, 2021. [11] X. Tan, M. Li, P. Zhang, Y. Wu and W. Song, Complex-Valued 3-D Convolutional Neural Network for PolSAR Image Classification, IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 6, pp. 1022- 1026, June 2020. [12] M. Imani, H. Ghassemian, Spectral-Spatial Classification of High Dimensional Images Using Morphological Filters and Regression Model, 6th International Conference on Intelligent & Advanced Systems (ICIAS2016), Kuala Lumpur, Malaysia, 15-17 August 2016. [13] Z. Zhang, H. Wang, F. Xu and Y. Jin, Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 12, pp. 7177-7188, Dec. 2017. [14] W. Tao, H. Yulin, W. Junjie, Y. Jianyu and L. Daifang, SAR ATR based on Generalized Principal Component Analysis Integrating Class Information, 2009 IET International Radar Conference, Guilin, pp. 1-4, 2009. [15] M. Imani, H. Ghassemian, Binary coding based feature extraction in remote sensing high dimensional data,Information Sciences, vol. 342, pp. 191-208, 2016 [16] 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. | ||
|
آمار تعداد مشاهده مقاله: 409 تعداد دریافت فایل اصل مقاله: 314 |
||