Polarimetric SAR Speckle Filtering Research Articles

Polarimetric SAR Speckle Filtering Based on Similarity Test and Adaptive Clustering

Speckle filtering of polarimetric synthetic aperture radar (PolSAR) data is a necessary preprocessing step for many subsequent applications. The performances of speckle reduction and details preservation are primarily determined by the scheme of similar pixel selection. This letter mainly contributes to establish a novel approach for adaptive and efficient selection of similar pixels. The core idea is to introduce the clustering concept to collect similar samples during the similarity test of polarimetric matrices which is functioned to be a distance measure. Adaptive clustering based on fast finding of density peaks of the data elements is adapted and an automatic determination strategy of cluster numbers is developed. Then the proposed speckle filter is established based on the distance measure and the adaptive clustering. Both unmanned aerial vehicle SAR (UAVSAR) and Radarsat-2 data sets are used for experimental studies. The demonstrations of similar pixel selection for several typical scattering patterns are carried out. The similar samples have been adaptively and precisely identified by the proposed scheme. Furthermore, the comparisons of speckle filtering performances with several advanced speckle filters clearly demonstrate the efficiency and superiority of the proposed method.

Polarimetric SAR Speckle Filtering and the Extended Sigma Filter

The advancement of synthetic aperture radar (SAR) technology with high-resolution and quad-polarization data demands better and efficient polarimetric SAR (PolSAR) speckle-filtering algorithms. Two requirements on PolSAR speckle filtering are proposed: 1) speckle filtering should be applied to distributed media only, and strong hard targets should be kept unfiltered; and 2) scattering mechanism preservation should be taken into consideration, in addition to speckle reduction. The purpose of this paper is twofold: 1) to propose an effective algorithm that is an extension of the improved sigma filter developed for single-polarization SAR; and 2) to investigate speckle characteristics and the need for speckle filtering for very high resolution (decimeter) PolSAR data. The proposed filter was specifically developed to account for the aforementioned two requirements. Its effectiveness is demonstrated with Jet Propulsion Laboratory airborne synthetic aperture radar data, and comparisons are made with a boxcar filter, the refined Lee filter, and a Wishart-based nonlocal filter. For very high resolution PolSAR systems, such as the German Aerospace Center F-SAR and Japanese Pi-SAR2, with decimeter spatial resolution, we found that the complex Wishart distribution is still valid to describe PolSAR speckle characteristics of distributed media and that speckle filtering may be needed depending on the size of objects to be analyzed. F-SAR X-band data with 25-cm resolution is used for illustration.

Multi-look Polarimetric SAR Speckle Filtering Based on Unsupervised Classification

Multi-look Polarimetric SAR Speckle Filtering Based on Unsupervised Classification

Classified pixel-based windowing algorithm for polarimetric SAR speckle filtering

Two main purposes of synthetic aperture radar (SAR) speckle filtering are the preservation of the edge sharpness and the suppression of the speckle noise. However, most of the existing filtering techniques suppress the speckle noise while smearing edges. This problem is partly solved by using nonsquare windows, but it can also generate large errors in many applications. A new windowing algorithm that can overcome the defects of the nonsquare window, by discriminating the edges more precisely with a preliminary terrain classification result, is proposed.

Polarimetric SAR speckle filtering and its implication for classification

This paper proposes a new approach in polarimetric synthetic aperture radar (SAR) speckle filtering. The new approach emphasizes preserving polarimetric properties and statistical correlation between channels, not introducing crosstalk, and not degrading the image quality. In the last decade, speckle reduction of polarimetric SAR imagery has been studied using several different approaches. All of these approaches exploited the degree of statistical independence between linear polarization channels. The preservation of polarimetric properties and statistical characteristics such as correlation between channels were not carefully addressed. To avoid crosstalk, each element of the covariance matrix must be filtered independently. This rules out current methods of polarimetric SAR filtering. To preserve the polarimetric signature, each element of the covariance matrix should be filtered in a way similar to multilook processing by averaging the covariance matrix of neighboring pixels. However, this must be done without the deficiency of smearing the edges, which degrades image quality and corrupts polarimetric properties. The proposed polarimetric SAR filter uses edge-aligned nonsquare windows and applies the local statistics filter. The impact of using this polarimetric speckle filtering on terrain classification is quite dramatic in boosting classification performance. Airborne polarimetric radar images are used for illustration.