remote sensing image change detection based on the combination of pixel-level and object-level analysis

Clicks: 183
ID: 182252
2017
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #79 of 105 articles by views in Phytochemistry

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In order to improve the change detection accuracy of the high resolution remote sensing image, a novel framework based on the combination of pixel-level and object-level analysis is proposed. Firstly, the two temporal images are superimposed, and the principal component analysis is performed. Then, it is utilized that the entropy rate segmentation algorithm to segment the first principal component image by changing the number of super-pixels to obtain the multi-layer super-pixel regions with different sizes. At the same time, by analyzing the difference of spectral feature and texture feature on two temporal images, it is used that adaptive PCNN neural network algorithm to make a fusion of the two difference images. Afterwards, the level set (CV) method is used to get the pixel-level change detection results. At last, the change intensity level quantization and decision level fusion are used on the initial change detection results with the region labeling matrix, serving as the post-processing part to obtain the changed objects. Experimental results on the sets of SPOT-5 multi-spectral images show that the new framework can effectively integrate the advantages of pixel-based and object-based image analysis methods, which can further improve the stability and applicability of the change detection process.
Reference Key
wenqing2017actaremote Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;FENG Wenqing;SUI Haigang;TU Jihui;SUN Kaimin
Journal Phytochemistry
Year 2017
DOI
10.11947/j.AGCS.2017.20160606
URL
http://html.rhhz.net/CHXB/html/2017-9-1147.htm
Keywords
change detection cartography

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.