The clustering problem of polarimetric SAR image is an optimization problem with high dimension and large amount of data. Aiming at the problem that the classical unsupervised classification methods for High Resolution Polarimetric SAR images are difficult to find the global optimal solution. The Particle Swarm Optimization (PSO) algorithm was proposed in High Resolution PolSAR images clustering. For the first beginning, the scattering eigenvalues of PolSAR data were used for initial classification, and then followed by the computation of clustering center and initialization of PSO algorithm, finally the particle swarm are introduced in the iterative steps to reduce noise effect and improve the classification results. The performance of this novel method is demonstrated in experiments using L-Band PolSAR image of San Francisco Bay.
Forest canopy structure is very important to measure forest change and forest coverage. And TerraSAR-X data are well suited for inversion applications at tree height. Based on the Random Volume over Ground model, the three-stage algorithm and its PSO improvement are studied in this paper. Taking the TerraSAR-X data of Mengla County in Yunnan Province China as the data source, the forest height inversion algorithm were compared in the experiment part. Finally, the results are verified with the field measured data. The results show that the precision of forest height inversion based on the PSO intelligent algorithm is better than the traditional three-stage algorithm, and the correlation coefficient is improved by more than 20%.
A novel methodology base on object-oriented MRF is proposed in order to obtain precise segmentation of high resolution satellite image. Conventional pixel-by-pixel MRF model methods only consider spatial correlation and texture of each pixel fixed square neighborhood. The segmentation method based on pixel-by-pixel MRF model usually suffers from salt and pepper noise. Based on the analysis of problems existing in pixel-by pixel MRF model methods of highresolution remote sensed images, an object-oriented MRF-based segmentation algorithm is proposed. The proposed method is made up of two blocks: (1) Mean-Shift algorithm is employed to obtain the over-segmentation results and the primary processing units are generated based on which the object adjacent graph (OAG) can be constructed.(2) MRF model is easily defined on the OAG, in which special features of pixels are modeled in the feature field model and the neighbor system, potential cliques and energy functions of OAG are exploited in the labeling model. The proposed segmentation method is evaluated on high resolution remote sensed image data-IKONOS. The experimental results show the proposed method can improve the segmentation accuracy while simultaneously obviating "salt and pepper noise" phenomenon and reducing the computational complexity greatly.
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