An End-to-End Error Model for Classification Methods Based on Temporal Change or Polarization Ratio of SAR Intensities
TL;DR: The results indicate that, for the applications under study, channel gain imbalance is usually not a decisive parameter, but radiometric stability is more critical in methods based on the temporal change, while Crosstalk has a negligible effect in the case of copolarizations.
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Abstract: This paper aims at defining the expression of the probability of error of classification methods using a synthetic aperture radar (SAR) intensity ratio as a classification feature. The two SAR intensities involved in this ratio can be measurements from different dates, polarizations, or, also possibly, frequency bands. Previous works provided a baseline expression of the probability of error addressing the two-class problem with equal a priori class probabilities and no calibration error. This study brings up a novel expression of the error, providing the possibility to assess the effect of class probabilities and calibration errors. An extended expression is described for the n -class problem. The effect of calibration errors such as channel gain imbalance, radiometric stability, and crosstalk is assessed in the general case. The results indicate that, for the applications under study, channel gain imbalance is usually not a decisive parameter, but radiometric stability is more critical in methods based on the temporal change. Crosstalk has a negligible effect in the case of copolarizations. The impacts of other system parameters, such as ambiguity ratio, time-lapse between repeat-pass orbits, spatial resolution, and number of looks, are illustrated through a set of assumptions on the backscattering values of the considered classes. The model is validated by comparing some of its outputs to experimental results calculated from the application of rice fields mapping methods on real data. This error model constitutes a tool for the design of future SAR missions and for the development of robust classification methods using existing SAR instruments.
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References
An entropy based classification scheme for land applications of polarimetric SAR
Shane R. Cloude,Eric Pottier +1 more
TL;DR: The authors outline a new scheme for parameterizing polarimetric scattering problems that relies on an eigenvalue analysis of the coherency matrix and employs a three-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data.
2.5K
A three-component scattering model for polarimetric SAR data
Anthony Freeman,S.L. Durden +1 more
TL;DR: An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations, which is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.
2.3K
Unsupervised classification using polarimetric decomposition and the complex Wishart classifier
TL;DR: The authors observed that the class centers in the entropy-alpha plane are shifted by each iteration, and are useful for class identification by the scattering mechanism associated with each zone.
965
A statistical and geometrical edge detector for SAR images
Ridha Touzi,A. Lopes,P. Bousquet +2 more
TL;DR: The decision threshold can be theoretically determined for a given probability of false alarm as a function of the number of looks of the image under study and the size of the processing neighborhood.
723
Change detection techniques for ERS-1 SAR data
Eric Rignot,J.J. van Zyl +1 more
TL;DR: Several techniques for detecting temporal changes in satellite synthetic-aperture radar (SAR) imagery are compared, using both theoretical predictions and spaceborne SAR data collected by the first European Remote Sensing Satellite, ERS-1 as mentioned in this paper.