Multitemporal Polarimetric SAR Data Fusion for Land Cover Mapping
XIE Chou 1, WAN Zi 1, XU Mao-song 2, XIA Zhong-sheng 3, ZHANG Feng-li 1
1.Institute of Remote Sensing Applications, Chinese Academy of Science, Beijing 100101, China; 2. Academy of Forestry Inventory, Planning and Designing, State Forestry Administration, Beijing 100714, China； 3. Forestry Resource Administration Station, Forestry Department of Guizhou Province, Guizhou 550001, China
With the development of the polarimetric Synthetic Aperture Radar (SAR), the research on the land cover classification using SAR data has developed rapidly. However, the classification accuracy is seriously affected by the speckle noises on the SAR image. A new method combining the advantages of multi temporal SAR data and quad-polarization SAR data is presented in this paper. A method of multitemporal SAR data fusion was used to eliminate the effect of the speckle noises on the SAR image. An area of 12 km×17 km was selected as the test area. 6 multi-temporal RADARSAT-2 images were used in this study to conduct the land cover classification. The results show that different land cover types represent different backscattering mechanisms, and the backscattering coefficient value of different land cover types varies as a function of time. Based on the fusion result of multi temporal polarimetric SAR data, the authors fulfilled the land cover classification, and the results show that this method can effectively distinguish such objects as man-made buildings, forests, farms and land and water. The speckle noise is obviously reduced and the visual appearance of the SAR fusion image is obviously improved.
［3］Lee J S, Grunes M R, Ainsworth T L, et al. Unsupervised Classification Using Polarimetric Decomposition and the Complex Wishart Classifier［J］. IEEE Transactions on Geoscience and Remote Sensing，1999, 37(5):2249-2258.
［4］Lee J S, Grunes M R, Pottier E, et al. Unsupervised Terrain Classification Preserving Polarimetric Scattering Characteristics［J］.IEEE Transactions on Geoscience and Remote Sensing，2004, 42(4):722-731.
［5］Bamler R, Attler B S.SAR Data Acquisition and Image Formation［M］.［s.l.］:［s.n.］,1993, 53-102.
［6］Moreira A, Schreiber R. Coregistration of Interferometric SAR Images Using Spectral Diversity［J］. TGARS，2000, 38(5):2179-2191.
［7］Touzi R, Lopes A, Bruniquel J, et al. Coherence Estimation for SAR Imagery［Z］. 1998.
2010, Vol. 22 
