Onderzoeksproject S0/00/053 (Onderzoeksactie S0)
Context and objectives
In polarimetric SAR interferometry (PolInSAR), polarimetric techniques are introduced into SAR interferometry applications to provide combined sensitivity to the vertical distribution of scattering mechanisms. As a result, it becomes possible to investigate the three-dimensional structure of vegetation cover using a polarimetric SAR. The topics to be covered in this three-year project include:
(1) PolInSAR processing;
(2) PolInSAR physics;
(3) PolInSAR fusion;
(4) PolInSAR potentialities in remote sensing.
Methodology
PolInSAR processing: PolInSAR theory (literature review, algorithmics and calibration issues), processor design and coding based on existing CSL tools, Selection of test data, processor testing on test data.
PolInSAR physics: literature review, polarisation states and coherent model analysis, scattering mechanism decomposition.
PolInSAR fusion: fusion of different frequency E-SAR PolSAR data as well as PolInSAR with Daedalus optical data for land cover classification and land cover feature recognition.
Results
A new software called PolInSAR has been developed, implemented and validated at the CSL taking (and often helping to develop) the ESA PolSARpro software as reference.The processor output the three optimized coherence images. At user choice, the processor can also output the three corresponding optimized interferograms, the H-A-alpha decompositions of each polarimetric acquisition and the HH, VV an XX amplitude images.
An improved version of the UCL polarimetric radiative transfer model, POLSCAT, and its associated code has been developed, in order to integrate the interferometric coherence information. Validation used JRC data as well as the polarimetric interferometric products delivered by the new CSL PolInSAR processor.
For both fusion methods the overall accuracy for each of the fused sets is better than the accuracy for the separate sets of features. Based on the results presented in this research we found that:
* Fused features from different SAR frequencies are complementary and adequate for land cover classification;
* PolInSAR features are complementary to the PolSAR information and essential for producing accurate classification of different land cover types as man-made object, water bodies, forest, crops and bare soils;
* The optical data is complementary information for the SAR data but not necessary for the production of accurate land-cover classification.
A brainstorming on PolInSAR potentialities for remote sensing, gathering the Consortium and the User Members of the Steering Committee, took place in October 2006. PolInSAR has been shown to have interesting potentialities for remote sensing in the fields of crisis management, humanitarian demining and agriculture, while this last application requires more progress in modelling, especially in soil moisture evaluation. The requirements of these three domains of application have been confronted with the PolInSAR products specificities, in order to provide the users with more adequate products.
