Research progress of PolSAR technology in IECAS
Institute Of Electronics, Chinese Academy Of Sciences, China, People's Republic of
Within the framework of the DRAGON project, the Institute of Electronics, Chinese Academy of Sciences (IECAS) continuously had a tight collaboration with the European and the Chinese partners. Joint research is around the 3 scientific topics: hybrid-polarity (HP) architecture, vegetation classification, and multi-aspect polarimeric scattering mechanism.
As to the study on hybrid-polarity (HP) architecture, an improved version of the HP architecture is proposed in order to overcome the two implementation difficulties concerning calibration issue and transmit configuration. Polarimetric SAR (PolSAR) systems based on this improved HP architecture do not only have mitigated transmit distortions through a pre-distortion technique, but also have the ability to transmit arbitrary polarizations, thus supporting all dual- and quad-polarimetric modes which uses orthogonal linear polarization basis in reception. Here, the supported dual-polarimetric modes include the circular transmit linear receive (CTLR) mode and π/4 mode of compact polarimetry. A prototype system has been built for further experimental verifications and experiments.
In the aspect of polarimetric classification, a supervised PolSAR image classification based on nearest-regularized subspace (NRS) and the vegetation classification based on the decision tree classifier are respectively studied. NRS is a representation-based classification approach. The polarimetric SAR feature space is constructed by the components of different target decomposition algorithms for each pixel, including those scattering components of Freeman, Huynen, Krogager, Yamaguchi, Arii decomposition, as well as the eigenvalues, eigenvectors and their consequential parameters such as entropy, anisotropy and mean scattering angle. Furthermore, because that all these representation-based methods were originally designed to be pixel-wise classifiers which only consider the polarimetric features while ignoring the spatial-contextual information, Markov Random Field (MRF) is also introduced into our scheme. Preliminary experiments display that the proposed NRS-MRF method outperforms other compared approaches with few training percent, including SVM, NRS and SVM-MRF. For vegetation classification applications, in order to make full use of PolSAR features’ scattering mechanism description, a decision tree classifier comes into use due to its simple and hierarchical structure. Since a decision tree classifier is flexible in terms of the discriminant rules, a hierarchical classification process based on Fisher Linear Discriminant (FLD) is built. Furthermore, the feedback adjustments of the classification results, such as feature combinations, partition algorithms and split sequences, make it possible to improve the accuracy of some specific classes. The experiment regarding the AIRSAR-Flevoland data illustrates that this method can obtain a good classification accuracy, and that it can extract useful information from the entire classification process in order to form new expert knowledge, such as the PolSAR features, classifying algorithms, and relationships between the classes.
Referring to the multi-aspect polarimeric scattering mechanism, the anisotropic scattering property is analyzed and two parameters both derived from traditional polarimetric entropy is proposed. One is called multi-aperture polarimetric entropy(MAPE), describes both depolarisation and directivity. The other is called sub-aperture entropy(SAE), describes change of depolarisation with look angle. These two parameters are used to separate targets which have both strong directivity and specific scattering mechanism, have specific scattering mechanism but not directivity, have neither directivity nor specific scattering mechanism. The effectiveness of the new parameters is validated with real polarimetric Circular SAR data, acquired by the Institute of Electronics airborne CSAR system at P-band.
Terrain Correction Methods For Multi-dimensional SAR Data Applied To Forest Above Ground Biomass Estimation
1The research Institute of Forest Resources Information Technique, Chinese Academy of Forestry,Beijing, China; 2Institute of Electronics, Chinese Academy of Sciences, Beijing, China; 3I.E.T.R -Univ Rennes 1, France
In this report, we will introduce the main research progress of forest above ground biomass (AGB) estimation study based on integration of multi-dimensional SAR data. It mainly contains the following three aspects. (1) We proposed a three-steps semi-empirical radiometric terrain correction approach for PolSAR data. The three steps of terrain effects correction are polarization orientation angle, effective scattering area, and angular variation effect corrections. Based on the LiDAR-derived forest AGB data, detailed analysis and evaluation were carried on for the three correction steps. (2) Base on the simplified InSAR decorrelation model (SINC model) and Algebraic Difference theory, we developed a terrain correction approach for coherence image of InSAR data. And the method was evaluated by space-borne and air-borne InSAR data. (3) Based on the X-band single-pass InSAR data and P-band PolSAR data acquired by multi-dimensional SAR system (CASMSAR) of China, we developed one combined estimation approach of forest AGB based on multi-dimensional SAR data by integrating the terrain correction methods proposed above.
Virtual Dispalacement Method for Tree Volume
Friedrich-Schiller-Universität Jena, Germany
Virtual Dispalacement Method for Tree Volume