Polarized SAR (synthetic aperture radar) image segmentation method with space adaptivity

Abstract

The invention provides a polarized SAR (synthetic aperture radar) image segmentation method with space adaptivity, mainly solving the problem that a segmentation result can not reflect image detail information caused by insufficient space complexity adaptivity by using the conventional segmentation technique. The polarized SAR image segmentation method comprises the following steps of: firstly, combining H / alpha-ML Wishart cluster and quadtree disassemble to obtain initial segmentation areas with unequal sizes and capability of adapting scene complexity, and regulating the sizes and the shapes of the initial segmentation areas by utilizing complex Wishart distribution and an MRF (markov random field) to obtain a final segmentation result. The method fully and effectively utilizes polarization information of a polarization SAR image with the excellent space adaptivity, the segmentation result can be used for well reserving detail information of a polarization SAR image, the segmentation speed is relatively quick and the segmentation result is relatively exact.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a space-adaptive polarization synthetic aperture radar (SAR) image segmentation method. Background technique [0002] Compared with the traditional synthetic aperture radar (SAR), polarimetric SAR uses the scattering information of multiple channels to obtain a more comprehensive understanding of the target. After decades of development, great progress has been made in the theoretical and applied research of polarimetric SAR. Various polarimetric SAR system platforms have acquired a large amount of ground data, and the analysis and interpretation of these multi-polarimetric data has become an important task in recent years. Research hotspots. Image segmentation is just one of the key steps. Due to the serious influence of coherent speckle noise, the segmentation of polarimetric SAR images is very difficult. Edge detection-based methods and pixel clustering-based methods ...

AI Technical Summary

Problems solved by technology

This method has high segmentation accuracy, fewer isolated pixels in the segmentation result, and better image connectivity, but its shortcomings are also very obvious.

This method does not consider the change of scene complexity in the image. In the initial segmentation, the image is divided into uniform rectangular blocks, and the global energy smoothing coefficient is used in the MRF. Region segmentation is poor

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