Lab Color Space Assignment for Decomposed Fully Polarization Pi-SAR Data

Cheng-Yen Chiang1,3

, Kun-Shan Chen2, Chih-Yuan Chu

3, Y. Yamaguchi

4, Kuo-Chin Fan

1

1Department of Computer Science and information Engineering, National Central University, Jhongli, Taiwan

2Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing, China

3G-AVE Technology Corp., Taipei, Taiwan

4Electrical and Information Engineering, Niigata University, Niigata, Japan

Abstract - Color encoding or assignment of multi-

polarization or fully polarimetric synthetic aperture radar

(PolSAR) image is vital for visual display and interpretation of the polarimetric information. In this paper, based on the Lab color space that is uniformly perceptual, we propose a color

assignment framework aiming at a better visual perception and information interpretation for Pi-SAR-L Quad-Pol data. It is shown that the new color assignment scheme not only

preserves the color tone of the polarization signatures, but also enhances the target information embedded in the total returned power. Using the property the Euclidean distance of

color, the five channels derived from four components decomposition method can be mapping to Lab color space intuitively.

Index Terms — Lab Color Space, Pi-SAR, Four Components Decomposition, Synthetic Aperture Radar.

1. Introduction

Color presentation to a color space for different frequency

or polarization channels is useful for analyzing the

elements within a single pixel by human eye. However, a

question generally raised is that what kind the color coding

scheme is the best or optimally suited for a particular

context and objective in a general image domain?

Target decomposition of a Quad-Pol SAR image proves a

powerful tool to characterize the terrain features. It is useful

to interpret the scattering mechanisms by applying the

coherent or model-based decomposition techniques [1-5].

For visual inspection and interpretation, a simple way is to display individual component monotonically. For PolSAR

image visualization, the main objective of color encoding

or color mapping is to explore the capability of quantitative

and qualitative detection or classification for objects of

mixed scattering mechanism in SAR image. In four

component decomposition [4-5], for example, a simple

RGB is commonly used in mapping three components:

double bounce, volume and surface scattering but the fourth

component, the helix scattering, is independently displayed

in grayscale, e.g, or simply by heuristic color mixing. So

the power of four components decomposition is not fully

presented in pseudo-color image space. The basic principle

is color composition without losing information offered by

target decomposition of scattering mechanisms. The

organization of this paper is as follows. In next section, we

give a rational why we chosen the Lab color space for fully

polarization data. The uniform perceptual color assignment,

Lab color space, and the framework of transformation from

information data to display device monitor was introduced

in section 2 also. Section 3 presents the Pi-SAR-L

experimental results and discussions. Finally, conclusion is

drawn from this study.

2. Color Encoding for PolSAR Images

In total, five channels data are generated, including total

power channels in the Yamaguchi’s four components

decomposition with rotation [4-5], known as Y4R. The four

components include volume, double bounce, surface and

helix scattering, each exhibits different behavior of

scattering mechanisms, while the total power channel

retains the geometrical properties of the target being

observed. The Lab color space is colorimetric, perceptually

uniform. Hence, it is intuitive to map the polarization

signature in CIE-Lab color space, which defines a*, b* and

brightness axis L* with respective to xyz-axis in Cartesian

coordinates, where a* extends from green to red, while b*

from blue to yellow. Referring to Fig. 1 we may assign the

total power to the brightness axis to represent the target’s

structure and thus to enhance the contrast of the boundary.

Basically, two stages are constructed in the proposed

color assignment framework: the first is mapping from

PolSAR data space onto perceptually uniform color space

with respect to the four component decomposition channels

and CIE-Lab color space; and the second is the

transformation between the perceptually uniform and

device dependent color spaces. Notice that because the

conversion in stage two is nonlinear, the loss of certain

color in the difference of gamut area is inevitable, and

irreversible.

Fig.1 Resemble of Poincare Sphere (above, from [6]) and

generic Lab Color Sphere (below).

The four decomposition components in Lab color space

such that the target information can be strongly and yet

effectively brought out for visual analysis. To begin with,

let us linearly assign the volume scattering to negative

value and double bounce scattering to positive value in the

a- axis, and in the b-axis the surface and helix scattering

components are assigned to negative and positive values,

respectively, followed by formulating the linear

combination for chroma of Lab color space taking into

account the perceptually uniform property.

When it comes to coding in Lab color space, it is possible

to align the channel of main interest to a* axis while

rotating the other channels away from b* axis. In Fig. 2a,

we see that the pairs Pv –Pd, Ps –Pc are aligned with a and b

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axis, respectively. Indeed, we might keep one pair

alignment fixed, while adjust the other pair out of a or b

axis. For example, in order to enhance a weaker scattering

component, e.g., helix scattering, we turn Pv and Pd 30

degrees away from a axis, but keep Ps -Pc aligned with b

axis as shown in Fig. 2b. Another option is to suppress the

scattering component that is of no interest. Fig. 2c is an

example to vanish the helix scattering by turning Pd 60

degrees away from a axis and Ps 30 degrees away from b

axis. From this example, we also see coding in Lab space

offers large flexibility depending upon users’ inclination

and objective.

Fig.2 Realignment of the four scattering components on a

color wheel of Lab color space. (a) all alignment, (b) Ps

alignment, (c) Pv alignment.

3. Experimental Result

We used a Pi-SAR-L data [8], which was acquired over a

calibration site with eight kinds corner reflectors deployed

at Tottori- dune, Japan on 4 October 2000. Fig. 3 shows a

comparison of color-encoding using sRGB and Lab for

Pauli basis composition, three components, with local and

global data slicing, 4 components decomposition. As in

previously case, Ps-Pc alignment was applied in Lab color-

encoding with 5% data slicing in total return power. In

sRGB color space, it can be seen that visual difference

exists when applying local and global data slicing. With the

local data slicing, the visual recognition can be improved

but paying the price of degrading identifiability of different

color channels. Recalled that in Lab color-encoding we

adjust the double and volume scattering with 30 degrees to

preserve, and enhance, the helix component.

Fig. 3 Comparison of color-encoding using sRGB and

Lab for (a) Pauli basis composition, (b) three components

with 5% local data slicing, (c) three components with 5%

global data slicing, and (d) four components decomposition

To further exploit the power of Lab, we analyze the

calibration site as enclosed by white rectangular in Fig. 4.

We see that from an array of corner reflectors, being strong

scattering targets, several targets tend to be in blue,

indicating surface scattering dominant in sRGB color space.

This of course is not true. This distortion is corrected, to

great extent, in Lab color space, where no surface scattering

was falsely appearing in corner reflectors.

Fig. 4 Comparison of sRGB and Lab color-encoding for

an array of corner reflectors indicated by enclosed white

rectangular

4. Conclusion

In this paper, we proposed a color-encoding framework for Pi-SAR-L Quad-Pol data based on a perceptually uniform Lab color space. In particular, Yamaguchi’s four components decomposition with rotation (Y4R) was adopted for target decomposition into four scattering mechanisms. It is found that not only the chroma by four components can be well preserved and presented, but also the inclusion of the total power enables us to enhance more correctly the targets structure. By so doing, the target contrast in the same scattering component but with different intensity can be more easily differentiated. It is advantageous to perform color encoding in Lab color space so that four scattering components can be displayed simultaneously to enrich the target information content for visualization. We can conclude that Lab color space is very suitable for color-encoding the PolSAR data. .

References

[1] A. Freeman and S. L. Durden, “A three component scattering model

for polarimetric SAR data”, IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 963- 973, 1998.

[2] Jong-Sen Lee and E. Pottier, Polarimetric Radar Imaging: From

Basics to Applications, CRC Press, 2009. [3] S. R. Cloude, Polarisation: Applications in Remote Sensing, Oxford

University Press, 2009.

[4] Y. Yamaguchi, T. Moriyama, M. Ishido, and H. Yamada, “Four

component scattering model for polarimetric SAR image

decomposition,” IEEE Trans. Geosci. Remote Sens., vol.43, no.8, pp.1699-1706, 2005.

[5] Y. Yamaguchi, A. Sato, W.-M. Boerner, R. Sato, and H. Yamada, “Four- component scattering power decomposition with rotation of

coherency matrix,” IEEE Trans. Geosci. Remote Sensing, vol. 49, no.

6, pp. 2251–2258, Jan. 2011 [6] S. Aïnouz, J. Zallat, A. De Martino, and C. Collet, “Physical

interpretation of polarization-encoded images by color preview,” Optics Express, vol. 14, no. 13, pp. 5916–5927, 2006.

[7] T. Lillesand, R. W. Kiefer, J. Chipman, Remote Sensing and Image

Interpretation, Wiley; 6 edition, 2007. [8] S. Makoto, U. Seiho, U. Toshihiko, M. Hideo, N. Akitsugu, K.

Tatsuharu, M. Takeshi, Manabe Takeshi, and M. Harunobu, “Flight Experiments of Airborne High-Resolution Multi-Parameter Imaging

Radar, Pi-SAR,” Journal of the Communications Research

Laboratory, vol.49, no.2 pp. 127-141, 2002

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