Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Abstract

Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of high-resolution PolSAR image segmentation. A novel segmentation algorithm is proposed in this study in order to address the problem and to obtain accurate and precise segmentation results. This method integrates statistical features into a fractal net evolution algorithm (FNEA) framework, and incorporates polarimetric features into a simple linear iterative clustering (SLIC) superpixel generation algorithm. First, spectral heterogeneity in the traditional FNEA is substituted by the G 0 distribution statistical heterogeneity in order to combine the shape and statistical features of PolSAR data. The statistical heterogeneity between two adjacent image objects is measured using a log likelihood function. Second, a modified SLIC algorithm is utilized to generate compact superpixels as the initial samples for the G 0 statistical model, which substitutes the polarimetric distance of the Pauli RGB composition for the CIELAB color distance. The segmentation results were obtained by weighting the G 0 statistical feature and the shape features, based on the FNEA framework. The validity and applicability of the proposed method was verified with extensive experiments on simulated data and three real-world high-resolution PolSAR images from airborne multi-look ESAR, spaceborne single-look RADARSAT-2, and multi-look TerraSAR-X data sets. The experimental results indicate that the proposed method obtains more accurate and precise segmentation results than the other methods for high-resolution PolSAR images.

Ten Years of Experience with Scientific TerraSAR-X Data Utilization

“…The goal of 27 percent of the proposals is to assess the polarimetric information in TerraSAR-X data. Some examples are the derivation of polarimetric information for mapping purposes [174], the improvement of the image segmentation of high-resolution polarimetric data [175,176], novel classification approaches [177] or texture tracking in polarimetric SAR data [178].…”

Edge Prior Multilayer Segmentation Network Based on Bayesian Framework

“…Through methods like Pauli decomposition [11] based on scattering matrix, the pseudo color and texture features in SAR imagery can be extracted, that many segmentation methods mainly for optical images can be utilised. Chen et al [12] proposed a multifeature segmentation for highresolution polarimetric SAR image based on fractal net evolution approach. Lang et al [13] developed the generalized mean shift algorithm for polarimetric SAR image segmentation.…”

Heterogeneity Measurement Based on Distance Measure for Polarimetric Sar Data

“…2009). While the complexity of polarimetric SAR data and the diversity of target scattering complicates the image interpretation and analysis (D'Hondt et al, 2013). Then, the image processing is the emphasis for the application of polarimetric SAR data, such as filtering, decomposition and classification, et al Simultaneously, because of the ability to objectively and quantitatively describe the difference of the scenes, many adaptive filters have been developed by utilizing the heterogeneity to enhance the performances of speckle filtering (Xing et al,2017, Lang et al,2015.…”