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High-resolution SAR (Synthetic Aperture Radar) image marking method based on supervised topic model

A subject model, high-resolution technology

Inactive Publication Date: 2012-07-04
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This is very difficult for massive high-resolution SAR images and requires a lot of manpower
At the same time, high-resolution SAR images have a large amount of latent semantic information, which is not considered by traditional labeling algorithms

Method used

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  • High-resolution SAR (Synthetic Aperture Radar) image marking method based on supervised topic model
  • High-resolution SAR (Synthetic Aperture Radar) image marking method based on supervised topic model
  • High-resolution SAR (Synthetic Aperture Radar) image marking method based on supervised topic model

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Embodiment Construction

[0033] The basic principles and related concepts of the high-resolution SAR image annotation method based on the supervised topic model are described below.

[0034] (1) Supervised Topic Model

[0035] Supervised topic models were originally used as a means in natural language analysis. The supervised topic model is a three-layer generative model. In the supervised topic model, there are the following definitions:

[0036] 1. Words: Words are the most basic elements processed in supervised topic models;

[0037] 2. Sight words: Sight words are the cluster centers of a series of similar words;

[0038] 3. Dictionary: a collection of all visible words;

[0039] 4. Document: a collection of words, which can also be expressed as a collection of visible words;

[0040] 5. Anthology: composed of a series of documents;

[0041] 6. Hidden theme: A series of visible words form a semantic theme, which is called a hidden theme;

[0042] 7. Response value: Define the response value...

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Abstract

The invention discloses a high-resolution SAR (Synthetic Aperture Radar) image marking method based on a supervised topic model. The high-resolution SAR image marking method comprises the following steps of: S1, image segmentation which further comprises the processes of segmenting an SAR image into a plurality of sub images with the same sizes and dividing each sub image into mutually-nonsuperposed rectangular regions, wherein each rectangular region is called a word; S2, image representation: generating files by using the sub images and generating a collected works by using all the files; S3, knowledge input which further comprises the processes of selecting a part of sub images with typical scenes to artificially mark based on key words, wherein a collection formed by the key words is called an implicit category of the files; S4, training reasoning: finishing the primary marking of the SAR image; and S5, semantic analysis: carrying out semantic analysis on the marking result through firstly validating semantic knowledge according to the classification result of all the sub images and the marking result of the words so as to obtain the marking result conforming to human semantics.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a high-resolution SAR image labeling method based on a supervised topic model. Background technique [0002] Synthetic Aperture Radar (SAR) has all-weather, all-time and imaging characteristics that can penetrate some ground objects, showing its superiority compared with optical sensors. Since the 1990s, SAR has been widely used in military and civilian applications. In recent years, a large number of high-resolution spaceborne and airborne SAR systems have been born, enabling a large number of high-resolution SAR images to be used to support surface surveying and mapping, urban change detection, natural disaster emergency response, anti-terrorism and stability maintenance, etc. SAR image annotation is a key step in SAR image processing. The traditional SAR annotation method is usually based on the human-computer interaction assisted interpretation exper...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/60
Inventor 王寰宇柳彬胡昊汪炜于秋则刘兴钊郁文贤
Owner SHANGHAI JIAO TONG UNIV
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