- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Multifrequency Polarimetric SAR Image Despeckling by Iterative Nonlocal Means Based on a Space-Frequency Information Joint Covariance Matrix
摘要： This paper presents an iterative nonlocal means (NLM) filtering method under the Bayesian framework to deal with the issue of multifrequency fully polarimetric synthetic aperture radar (PolSAR) image despeckling. Differing from most of the PolSAR filters designed for single-frequency data, the proposed NLM method is developed based on a space-frequency information joint covariance matrix, which can not only utilize multifrequency polarimetric information but also exploit the correlation between any two pixels in an image patch. Furthermore, with the aim of accelerating the filtering procedure and better retaining image details, an effective preselection step is employed. The filtering results obtained with both a simulated dataset and real multifrequency PolSAR datasets acquired by the AIRSAR system confirm the good performance of the proposed method in both reducing speckle and retaining details, when compared with some of the state-of-the-art despeckling algorithms.
关键词： Nonlocal means (NLM),polarimetric synthetic aperture radar (PolSAR),speckle filtering,Wishart distribution
更新于2025-09-09 09:28:46
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[IEEE 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) - Coimbatore (2018.3.29-2018.3.31)] 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) - Analysis and Evaluation of Speckle Filters by Using Polarimetric Synthetic Aperture Radar Data Through Local Statistics
摘要： Speckle noise, a granular noise, occurs in synthetic aperture radar (SAR) data due to the interference of reflected signals with several scatterers in a resolution cell. One of the simplest techniques to suppress speckle noise from polarimetric SAR data is to use local statistics. The Lee filter employs sample mean and variance of pixels of data degraded by speckle noise, which can be multiplicative, additive, or a mixture of both, in a searching window. The refined Lee method utilizes directional windows with local minimum mean square error (LMMSE), which ensures superior maintenance of spatial resolution and features. The Lee–Sigma filter is constructed on the basis of the two-sigma probability, which effectively suppresses the speckle noise. However, insufficiencies were observed in the generation of a biased evaluation, blurring of edges, and suppression of point targets. To eliminate these insufficiencies, the improved Lee–Sigma filter was developed, which employs the minimum mean square error estimate as a priori mean. Thus, an excellent maintenance of point targets and subtle details is displayed. In this study, the Lee, refined Lee, Lee–Sigma, and improved Lee–Sigma filters were evaluated using full polarimetric data. The evaluated results indicated that the improved Lee–Sigma filter performed better than other local statistics filters.
关键词： Speckle noise,Synthetic aperture radar,Minimum mean square error,Polarimetric synthetic aperture radar
更新于2025-09-04 15:30:14
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Polarimetric Convolutional Network for PolSAR Image Classification
摘要： The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix are used as the main research object to extract features. In this paper, we focus on the original polarimetric scattering matrix and propose a polarimetric scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on the convolution network to combine this feature. Based on the polarimetric scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for polarimetric scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding.
关键词： convolution network,polarimetric synthetic aperture radar (PolSAR),Classification,polarimetric scattering matrix
更新于2025-09-04 15:30:14