A Semiparametric Model for Hyperspectral Anomaly Detection
Using hyperspectral (HS) technology, this paper introduces an autonomous scene anomaly detection approach based on the asymptotic behavior of a semiparametric model under a multisample testing and minimum-order statistic scheme. Scene anomaly detection has a wide range of use in remote sensing appli...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/425947 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849404207827779584 |
|---|---|
| author | Dalton Rosario |
| author_facet | Dalton Rosario |
| author_sort | Dalton Rosario |
| collection | DOAJ |
| description | Using hyperspectral (HS) technology, this paper introduces an autonomous scene anomaly detection approach based on the asymptotic behavior of a semiparametric model under a multisample testing and minimum-order statistic scheme. Scene anomaly detection has a wide range of use in remote sensing applications, requiring no specific material signatures. Uniqueness of the approach includes the following: (i) only a small fraction of the HS cube is required to characterize the unknown clutter background, while existing global anomaly detectors require the entire cube; (ii) the utility of a semiparematric model, where underlying distributions of spectra are not assumed to be known but related through an exponential function; (iii) derivation of the asymptotic cumulative probability of the approach making mistakes, allowing the user some control of probabilistic errors. Results using real HS data are promising for autonomous manmade object detection in difficult natural clutter backgrounds from two viewing perspectives: nadir and forward looking. |
| format | Article |
| id | doaj-art-994a1fff8f8d4ecb833ea1d70f8662a1 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-994a1fff8f8d4ecb833ea1d70f8662a12025-08-20T03:37:03ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552012-01-01201210.1155/2012/425947425947A Semiparametric Model for Hyperspectral Anomaly DetectionDalton Rosario0SEDD/Image Processing Division, Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783, USAUsing hyperspectral (HS) technology, this paper introduces an autonomous scene anomaly detection approach based on the asymptotic behavior of a semiparametric model under a multisample testing and minimum-order statistic scheme. Scene anomaly detection has a wide range of use in remote sensing applications, requiring no specific material signatures. Uniqueness of the approach includes the following: (i) only a small fraction of the HS cube is required to characterize the unknown clutter background, while existing global anomaly detectors require the entire cube; (ii) the utility of a semiparematric model, where underlying distributions of spectra are not assumed to be known but related through an exponential function; (iii) derivation of the asymptotic cumulative probability of the approach making mistakes, allowing the user some control of probabilistic errors. Results using real HS data are promising for autonomous manmade object detection in difficult natural clutter backgrounds from two viewing perspectives: nadir and forward looking.http://dx.doi.org/10.1155/2012/425947 |
| spellingShingle | Dalton Rosario A Semiparametric Model for Hyperspectral Anomaly Detection Journal of Electrical and Computer Engineering |
| title | A Semiparametric Model for Hyperspectral Anomaly Detection |
| title_full | A Semiparametric Model for Hyperspectral Anomaly Detection |
| title_fullStr | A Semiparametric Model for Hyperspectral Anomaly Detection |
| title_full_unstemmed | A Semiparametric Model for Hyperspectral Anomaly Detection |
| title_short | A Semiparametric Model for Hyperspectral Anomaly Detection |
| title_sort | semiparametric model for hyperspectral anomaly detection |
| url | http://dx.doi.org/10.1155/2012/425947 |
| work_keys_str_mv | AT daltonrosario asemiparametricmodelforhyperspectralanomalydetection AT daltonrosario semiparametricmodelforhyperspectralanomalydetection |