Polarimetric SAR speckle filtering and its implication for classification

Abstract

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.

Detection of ice types in the Eastern Weddell Sea by fusing L- and C-band SIR-C polarimetric quantities

“…Speckle filtering using a Lee-refined polarized filter (Lee, Grunes, and Grandi 1999) is applied for speckle reduction, which reduces the speckle noise by the use of edge-aligned non-square windows and the application of a local statistics filter. Because the span of the incidence angle is as small as approximately 6°, an angular correction has not been performed.…”

Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints

“…To reduce the speckle, a 5 Â 5 boxcar filter is adequate as it is easy to implement. However, by considering the influence of spatial resolution, other elaborate filters, such as the refined Lee filter (Lee, Grunes, and De Grandi 1999), are recommended. (2) Segment the PolSAR image by SLIC superpixel algorithm.…”

Improved Classification of Polarimetric SAR Data Based on Four-component Scattering Model

“…Some edge-aligned nonsquare windows and local statistics will be used in this method. It has been shown that speckle filtering improves clustering [10]. [1 ] proposed a four-component scattering model for polarimetric SAR image decomposition based on the Freeman and Durden decomposition [2].…”

“…SPECKLE FILTERING Lee [10] proposed a dramatic approach in Polarimetric SAR speckle filtering. This paper will apply this filtering method, which emphasizes preserving polarimetric properties and statistical correlation between channels, and not degrading the image quality.…”

“…Two clusters will be merged if they have the shortest "Euclid" distance, and meanwhile they must be in the same scattering category. To prevent a class from growing too large and overwhelming the other classes, we define the size of classes to not larger than: Nmax = NNfinal (10) N is the total number of pixels in the POLSAR image, and Nfinal is the number of final classification, which is assigned by " 18?" in this paper.…”