1 / 21

IGARSS 2011 Vancouver, 24-29 July

A ROBUST SPECTRAL TARGET RECOGNITION METHOD FOR HYPERSPECTRAL DATA BASED ON COMBINED SPECTRAL SIGNATURES. IGARSS 2011 Vancouver, 24-29 July. Xiao Fan, Ye Zhang, Feng Li, Yushi Chen, Tao Shao, Shuang Zhou from Harbin Institute of Technology, China. Content. Motivation. Techniques.

tamar
Download Presentation

IGARSS 2011 Vancouver, 24-29 July

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A ROBUST SPECTRAL TARGET RECOGNITION METHOD FOR HYPERSPECTRAL DATA BASED ON COMBINED SPECTRAL SIGNATURES IGARSS 2011 Vancouver, 24-29 July Xiao Fan, Ye Zhang, Feng Li, Yushi Chen, Tao Shao, Shuang Zhou from Harbin Institute of Technology, China

  2. Content Motivation Techniques Method & System Experiments & Results Conclusions

  3. Motivation • Spectral Target recognition • Importance • important application for Hyperspectral Image Processing • Goal • high accuracy & robustness • Problem • spectral variation by complicated imaging environment • Solution

  4. Content Motivation Techniques Method & System Experiments & Results Conclusions

  5. Techniques • Support Vector Data Description (SVDD) • Inspired by Support Vector Machine (SVM) • A learning machine, first used for anomaly detection in hyperspectral image processing • A detector for spectral target recognition • Alleviate the heterogeneous spectra within homogeneous object

  6. Techniques • Spectral signatures • Reflective spectra, most common signatures • Relevant to physical and chemical properties • Illumination variation and terrain undulation • Spectral-amplitude fluctuation • Derivative spectra • Insensitivity to spectral amplitude; sensitivity to spectral slope

  7. Content Starting point Techniques Method & System Experiments & Results Conclusions

  8. Method & System • Combined spectral signatures • Simply connecting, curse of dimensionality • Combining on gray decision level • Combined weights of the signatures • Based on the role of each signatures • According to the data characteristic

  9. Content Starting point Techniques Method & System Experiments & Results Conclusions

  10. Experimental Data

  11. Experiment 1 Unavoidable noise makes heterogeneous spectra within the homogeneous object SVDD detector vs spectral match-based detector SAM SID SVDD with linear, quadratic polynomial, and cubic polynomial kernel

  12. Experiment 1 • Area under the ROC curve with Pf from 0 to 1

  13. Experiment 2 • Illumination variation and terrain undulation make the spectral-amplitude fluctuation • derivative spectra vs reflective spectral • mean spectral variance

  14. Experiment 2 • Area under the ROC curve with Pf from 0 to 1

  15. Experiment 3 • Combined the two spectral signatures by different weights • Equal weights • Unequal weights

  16. Experiment 3 • Area under the ROC curve with Pf from 0 to 1

  17. Thank You Email: fancy_2626@163.com

More Related