A Sparse Manifold Classification Method for Multi-scale Description Primitives of Polarized SAR Images

A classification method, multi-scale technology, applied in the field of image processing, which can solve problems such as poor model performance
CN106682701BActive Publication Date: 2019-06-11WUHAN UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Patents(China)
Current Assignee / Owner
WUHAN UNIV
Publication Date
2019-06-11

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Abstract

The invention discloses a sparse manifold classification method for multi-scale description primitives of polarimetric SAR images, in order to solve the problems of extraction and fusion of non-coherent information and coherent information of time-series polarimetric SAR Multiplicative model non-coherent problem, through an essential feature fusion and manifold sparse expression, can effectively classify time series polarimetric SAR images. The invention discloses a method for constructing a multi-scale description primitive that combines two scale information of polarization incoherent features and time series coherent features, and utilizes a multi-level nonlinear production model expressed by compressed sensing and sparse manifolds to The method of feature extraction and information dimension reduction can effectively classify time series polarimetric SAR images, and multi-scale description primitives can also become a general basic technology for time series polarimetric SAR image processing.
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Description

technical field

[0001] The invention belongs to the technical field of image processing, in particular to a sparse manifold classification method based on multi-scale description primitives of time series polarization SAR images. Background technique

[0002] Synthetic Aperture Radar (SAR) is a radar system used for imaging ground targets. With its high-resolution, all-time and all-weather characteristics, SAR has become an important tool for ground observation. SAR image classification is an important part of remote sensing image interpretation, and it is widely used in agricultural and forestry planning, disaster monitoring, environmental protection, military reconnaissance and other fields.

[0003] For a single SAR image, statistical distribution is an important classification method; for polarimetric SAR images, polarization decomposition is a common classification method; for multiple time series images, coherent information can be obtained by unwrapping interference ...

Claims

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