Weighted synthetic kernel and triple markov field (TMF) based polarimetric synthetic aperture radar (SAR) image classification method

Abstract

The invention discloses a weighted synthetic kernel and TMF based polarimetric SAR image classification method and relates to polarimetric SAR image classification. The method comprises the steps of step 1, selecting polarimetric SAR image polarization characteristics and training samples, and building characteristic space; step 2, establishing the weighted synthetic kernel; step 3, achieving initial classification by the combination of the weighted synthetic kernel and a support vector machine to be used as an initial value of a marking field X; step 4, estimating a novel marking field X and a novel auxiliary field U; step 5, using the marking field X as the final polarimetric SAR image classification result till the marking field X converges. According to the method, the problems that the initial classification accuracy is not high, and the Markove field cannot process polarimetric SAR image unsteady characteristics of the prior method are mainly solved. Homogeneous region classification results are smooth, marginal information can be kept well, the classification accuracy is improved apparently, and the method can be used for target detection and recognition of polarimetric SAR images.

Description

technical field [0001] The invention belongs to the technical field of radar polarization and image processing, relates to polarization SAR (synthetic aperture radar, synthetic aperture radar) image classification, in particular to a polarization based on weighted synthetic kernel and TMF (Triple Markov Field, triple Markov field) The SAR image classification method can be used for target detection and recognition of polarimetric SAR images. Background technique [0002] Image classification is one of the important contents of polarimetric SAR image interpretation, which has been widely used in military and civilian fields. Classification methods have always been a hot spot in frontier research in this field. Many polarimetric SAR image classification methods have been constructed by using the polarization scattering characteristics of ground objects and the classification methods in the field of pattern recognition. [0003] According to whether training data is needed, po...

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Problems solved by technology

At present, the algorithms involved in polarimetric SAR image classification include: traditional image processing algorithms, representative algorithms include mean value clustering, ISODATA algorithm, watershed algorithm, graph theory method, etc. Although these methods are based on theoretically mature classifiers, they are not fully Using the target scattering mechanism to classify polarimetric SAR images; the classification method based on neural network, the advantage of neural network is that it can directly establish a general classification method through category training samples without prior knowledge, although it can get a good Classification results, but this method has the problem of time-consuming calculation; based on the SVM (Support Vector Machine) method, the Vapnik research group proposed SVM in 1995, and Fukuda et al first used SVM for polarization SAR image classification. Later, some scholars combined SVM with quadtree, region merging, MRF (Markov Random Field Markov random field) field, etc., and obtained good classification results, but they can also be optimized to get better results; The classification method based on polarization target decomposition, this method can directly use the polarization characteristics of the target, and can w

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