Semi-supervised polarized SAR image classification method based on random forest composition

A technology of random forest and classification method, applied in the field of image processing, can solve the problems of not considering the spatial information of image sample points, poor classification effect, and inability to accurately represent the structure relationship of SAR data, so as to reduce the use of labeled samples, The effect of similarity relationship graph affinity and improving classification accuracy
CN107358142AActive Publication Date: 2017-11-17XIDIAN UNIV
4 Cites 22 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XIDIAN UNIV
Publication Date
2017-11-17

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The invention discloses a semi-supervised polarized SAR image classification method based on random forest composition. The method mainly solves a problem that a conventional classification method has a defect in representation of a similarity relation between sample points and does not utilize spatial information. The method comprises the following steps of: inputting raw data of a polarized SAR image; extracting relevant features of the data to obtain a data set; constructing an initial random forest model; training two classifiers by using two different sample sets with different attributes to help to train a semi-supervised random forest mode; optimizing the semi-supervised random forest model; constructing a similarity relation graph; constructing a spatial information graph; combining the similarity relation graph and the spatial information graph to obtain a similarity relation matrix between the sample points; and classifying the images and calculating a correct rate. The method constructs an amiable similarity relation graph and spatial information by using the semi-supervised random forest algorithm, improves the classification accuracy of the polarized SAR image, and can be used in civilian and military fields such as geological exploration, disaster relief, target identification and the like.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The invention belongs to the technical field of image processing, and mainly relates to classification of polarimetric SAR images, in particular to a semi-supervised classification method of polarimetric SAR images based on random forest composition, which can be used for object classification and target recognition of polarimetric SAR images. Background technique

[0002] Polarization SAR is a microwave imaging radar that utilizes the principle of synthetic aperture to achieve high resolution. Texture features and obvious geometric structures of ground objects can be widely used in many fields such as military affairs, agriculture, navigation, and geographical surveillance. It is highly valued in the field of international remote sensing, so polarimetric SAR image classification has become an important research direction of polarimetric SAR information processing.

[0003] The purpose of polarimetric SAR image classification is to use the polarizatio...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More