Abstract

Color enhancement of decomposed fully polarimetric synthetic aperture radar (PolSAR) image is vital for visual understanding and interpretation of the polarimetric information about the target. It is common practice to use RGB or HIS color space to display the chromatic information for polarization-encoded, Pauli-basis images, or model-based target decomposition of PolSAR images. However, to represent the chroma for multi-polarization SAR data, the region of basic RGB color space does not fully cover the human perceptual system, leading to information loss. In this paper, we propose a color-encoding framework based on the CIE-Lab, a perceptually uniform color space, aiming at a better visual perception and information exploration. The effective interpretability in increasing chromatic, and thus visual enhancement, is presented using extensive datasets. In particular, the four decomposed components—volume scattering, surface scattering, double bounce, and helix scattering—along with total return power, are simultaneously mapped into the color space to improve the discernibility among the scattering components. The five channels derived from the four-component decomposition method can be simultaneously mapped to CIE-Lab color space intuitively. Results show that the proposed color enhancement not only preserves the color tone of the polarization signatures, but also magnifies the target information embedded in the total returned power.

Highlights

  • Color enhancement for different bands or polarization channels is powerful for analyzing the elements within an image pixel by the human eye

  • Color encoding has been extensively used in multispectral remote sensing data [1,2,3]

  • Color encoding was designed in mapping the polarization information to retain their qualitative features in an effort of detecting targets in scattering media [7]

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Summary

Introduction

Color enhancement for different bands or polarization channels is powerful for analyzing the elements within an image pixel by the human eye. Image classification by measuring the color differences in optical multispectral remote sensing data using CIE-Lab color space was suggested in [6].

Results
Conclusion
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