The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 07, 2023

Filed:

Oct. 14, 2020
Applicant:

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, CN;

Inventors:

Bo Yu, Beijing, CN;

Fang Chen, Beijing, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06V 20/13 (2022.01); G06V 10/56 (2022.01); G06V 10/60 (2022.01); G06V 10/34 (2022.01);
U.S. Cl.
CPC ...
G06V 20/13 (2022.01); G06K 9/6262 (2013.01); G06V 10/34 (2022.01); G06V 10/56 (2022.01); G06V 10/60 (2022.01);
Abstract

This invention discloses a practical method for landslide detection in large space, which comprises the following steps: image synthesis, ice and snow detection, removal of non-potential landslide area, detection of potential landslide area, feature calculation, landslide detection model construction and precision validation; this invention avoids radiometric correction and outlier by detecting landslide from synthetic image. That guarantees practical applicability of the proposal. Firstly, detecting potential landslides can avoid the imbalanced sample distribution issue between background objects and landslides when training the landslide detection model. The landslide is further detected by building a random forest model based on the spectral features and textural features of potential landslide pixels in different neighboring time domains. It fully considers the changes of objects in different time domains, and lays a foundation for efficient landslide extraction. This model is relatively reliable and practical for automatically detecting landslide from large-scale images.


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