Polarimetric SAR (synthetic aperture radar) image segmentation based on DBN (deep belief network)

Abstract

The invention discloses a polarimetric SAR (synthetic aperture radar) image segmentation method based on a DBN (deep belief network). The advantages of learning features of the deep learning theory are applied to the polarimetric SAR image segmentation. The segmentation method includes subjecting polarimetric SAR data to fine Lee filtering; subjecting polarimetric SAR coherence matrix T to H/Alpha resolving to obtain the parameter feature; extracting gray-level co-occurrence matrixes from three channels on leading diagonals of the coherence matrix T, and calculating features including contrast, coherence, energy and inverse difference; combining the features and the elements of the coherence matrix to train a two-layer DBN; inputting the polarimetric SAR data in the DBN for classification and displaying a classification result image. According to the arrangement, the scattering feature and the gray-level co-occurrence matrix feature are integrated, information integrity is kept, and the layer-by-layer learning feature is applicable to polarimetric SAR image object recognition.

Description

technical field [0001] The invention belongs to the field of SAR (Synthetic Aperture Radar) image processing, in particular to a method involving polarization SAR image segmentation, which can be applied to target recognition and classification. Background technique [0002] As an important tool, Synthetic Aperture Radar (SAR) technology is widely used in military detection, resource exploration, urban development planning and marine research. Compared with single-polarization SAR, polarimetric SAR performs full-polarization measurement, which can fully obtain target scattering characteristic information by using the vector characteristics of electromagnetic waves. The emergence of polarimetric SAR has greatly expanded the application field of SAR. People can extract more information about geophysics from polarimetric SAR and apply it in various civil and military fields. With the use of more and more space-borne and airborne polarization systems, a large amount of polariza...

AI Technical Summary

Problems solved by technology

[0008] The technical solution for realizing the object of the present invention is: combining the scattering characteristics (coherence matrix elements and H / α decomposition parameters) and digital image characteristics (parameters of the gray co-occurrence matrix) of the polarimetric SAR image to ensure the integrity of the image information , fully excavate the texture information in the image, and construct a DBN model composed of multiple unsupervised models (here refers to RBM, restricted Boltzmann machine), which effectively overcomes the traditional neural network's easy convergence to local optimum and complex calculation. and other defects

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