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A Polarization SAR Image Classification Method Based on Multi-Feature Fusion

A technology of multi-feature fusion and classification method, which is applied in the field of polarimetric SAR image classification based on multi-feature fusion, and can solve the problems of different space, increased computing capacity, and inability to optimally separate feature vectors.

Active Publication Date: 2019-11-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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  • Application Information

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Problems solved by technology

Typical dimensionality reduction methods include linear dimensionality reduction methods and manifold learning methods. Linear dimensionality reduction methods are based on the Gaussian hypothesis, but real data are often in complex nonlinear spaces, requiring nonlinear methods to explore potential structures, and The linear dimensionality reduction method does not have the ability to cover the geometry and local distribution structure in the data space, and this information is crucial for classification; the manifold learning method in the nonlinear dimensionality reduction method assumes that the features can be retained at a low In the three-dimensional manifold structure, however, the inherent unsupervised and non-discriminative nature of manifold learning makes it difficult to apply in practice, and the feature vectors of different categories cannot be separated optimally, which is easy to cause misclassification and misclassification; (4) combining The most prominent problem in the two types of features is how to effectively combine different types of features, which requires the so-called information fusion technology
The difficulty of information fusion is that the space of different features is different, and the corresponding kernel functions are also different; the second is that the original feature information may be destroyed, especially when the magnitude of the two features differs greatly; The increase in capacity will easily lead to a decrease in computing efficiency
At present, there is no in-depth study on the information fusion method in the interpretation of polarimetric SAR images

Method used

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  • A Polarization SAR Image Classification Method Based on Multi-Feature Fusion
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Embodiment

[0052]The PolSAR (high-resolution polarimetric synthetic aperture radar image) data used in this embodiment is the C-band full-polarization SAR image of the Dutch Flevoland area acquired by the RadarSat-2 system in the four-polarization fine mode (resolution 5.2×7.6m) , in order to verify the implementation performance of the present invention, a region is selected from the fully polarized SAR image as the region to be classified, wherein the size of the region to be classified is 700×780, image 3 is a pseudo-color image obtained by Pauli decomposition (polarization target decomposition) of the area to be classified, Figure 4 is the corresponding ground truth reference map. The selected area to be classified includes four main features, which are: buildings, forests, farmland and water bodies. Figure 4 different grayscale regions in . At the same time, the pixels accounting for 1% of the full PolSAR image (known object types) are selected as the training sample set, and t...

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Abstract

The invention discloses a polarization SAR image classification method based on multi-feature fusion. The present invention firstly extracts the polarization feature vector of the polarimetric SAR image to be classified to obtain a high-dimensional polarization feature set; extracts the morphological section feature vector of the image SPAN processing result to obtain a high-dimensional morphological feature set; After the features are subjected to the dimensionality reduction processing of the locality-preserving discrimination analysis, the image pixels with known category labels are selected to form the training sample set, and then the remaining pixels of the entire image are selected as the classification sample set; the two types of low-dimensional features are respectively passed through the The SVM of the maximum posterior probability obtains the category label and the corresponding posterior probability of the pixel points in each case; the summation criterion or the adaptive weighted summation criterion is used to combine the posterior probability vectors of each pixel in the two situations According to the principle of maximum a posteriori probability, the final classification result of high-resolution polarimetric SAR images is obtained. The implementation of the invention helps to improve the classification accuracy and efficiency of resolving polarimetric SAR images.

Description

technical field [0001] The invention belongs to radar image processing and interpretation technology, that is, ground object classification processing is performed on high-resolution polarization synthetic aperture radar images, and in particular relates to a polarization SAR image classification method based on multi-feature fusion. Background technique [0002] In recent years, my country has successfully applied Synthetic Aperture Radar (SAR) in various fields. The successful application of SAR depends on effective SAR image processing and interpretation technology, and its premise is the classification technology of SAR image. Through classification, the ground object information in SAR images can be effectively obtained, and it can provide assistance for various applications such as urban planning, crop and forest observation, disaster assessment, and ground target recognition. Therefore, SAR image classification technology occupies a very important position. [0003] ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 曹宗杰丁尧冯籍澜崔宗勇
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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