In:
Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 88, No. 3 ( 2022-03-01), p. 199-205
Abstract:
In object-oriented information extraction from high-resolution remote sensing images, the segmentation and classification of images involves considerable manual participation, which limits the development of automation and intelligence for these purposes. Based on the multi-scale segmentation
strategy and case-based reasoning, a new method for extracting high-resolution remote sensing image information by fully using the image and nonimage features of the case object is proposed. Feature selection and weight learning are used to construct a multi-level and multi-layer case library model of surface cover classification reasoning. Combined with image mask technology, this method is applied to extract surface cover classification information from remote sensing images using different sensors, time, and regions. Finally, through evaluation of the extraction and recognition
rates, the accuracy and effectiveness of this method was verified.
Type of Medium:
Online Resource
ISSN:
0099-1112
DOI:
10.14358/PERS.20-00104R3
Language:
English
Publisher:
American Society for Photogrammetry and Remote Sensing
Publication Date:
2022
detail.hit.zdb_id:
188870-5
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