The idea is to consolidate the interacting distortions and, more importantly, isolate the system characteristics from specific targets.
Combine the distortions and simplify the system equation into its vector form (D is a 4x4 matrix, and S is a 4-element vector) View the system as linear transformation Evaluate the bias in the whole target space
Considering the natural, reciprocal targets, the vector of target response is represented as its feature vector. A4 is a 4x3 adapter matrix
It becomes easier to analyze the impact of different distortions. Next, we can visualize the relation between the MNE and a specific distortion.
We can view the contours as the projection of possible measurement errors in terms of the distortion. Both the phase and the magnitude need to be carefully calibrated.
With this distortion structure, there is a linear relation between MNE and the isolation level.
The residue Faraday angles should be less than 3 deg.
Polarimetric decomposition methods provide valuable tools to understand the scattering mechanisms of the targets. How would the decomposition methods alter the metrics? Or in other words, what is the specific needs for the error budgets for a given decomposition method? Even though the MNE is invariant to orthogonal basis change, the error budget will be different if the decomposition bases are not orthogonal. This evaluation framework cannot be applied to covariance matrix based decompositions where the decomposition bases undefined.
From now on, we will check the decomposition errors which vary with the decomposition methods. A tight upper bound: targets corresponding to the maximum error exist in natural scene. Over the whole image, the relative strength of the three decomposition channels keeps intact. Viewed as composition image, the target scene has little perceivable difference. Quantifying individual channel
Large biases appear in both double bounce and helix scattering channels. Double bounce: actually oriented di-plane.
For the covariance derived decomposition, we have to resort to the typical targets, like those representing the canonical scattering mechnisms.
Given these different behaviors in response to polarization distortions, we are thinking whether the MNE can be used as a baseline even for polarimetric decompositions.
We take the targets from the E-SAR image for the three canonical scattering mechanisms, and set a wide practical polarization artifacts, then simulate the decomposition errors and check them against the MNE metric.
The MNE seems form a good baseline for the quality assessment, for PolSAR imagery and its decompositions. For Krogager decomposition, we may need more attention on the phase terms of the cross-talk terms. For Freeman-Durden decomposition, the interpretation may be problematic if the evaluated MNE is higher. But we can find a general boundary of -25 dB to -20 dB as the requirement of polarization quality.
We feel this study prompts the use of this metric for requirement analysis and quantified quality asssement.
Transcript
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EVALUATION OF SYSTEM POLARIZATION QUALITY FOR POLARIMETRIC SAR IMAGERY AND TARGET DECOMPOSITION Yanting Wang, Thomas L. Ainsworth and Jong-Sen Lee Naval Research Laboratory
The apparent errors on the measurements depend on the distortion type and the targets
Channel Imbalance Cross-talk Observations Surface Channel Double bounce Channel Cross-pol Channel To investigate the impact of polarization quality is not trivial task in a practical system.
The maximum normalized error can be applied to evaluate the decomposition errors given a known set of decomposition bases
In case of orthogonal decomposition bases, or effectively orthogonal polarization basis change, the maximum decomposition error is same as the MNE. Pauli based decomposition falls into this category.
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