Localizing targets of interest in a given hyperspectral (HS) image has applications ranging from remote sensing to surveillance. This task of target detection leverages the fact that each material/object possesses its own characteristic spectral response, depending upon its composition. As signatures of different materials are often correlated, matched filtering based approaches may not be appropriate in this case. In this work, we present a technique to localize targets of interest based on their spectral signatures. We also present the corresponding recovery guarantees, leveraging our recent theoretical results. To this end, we model a HS image as a superposition of a low-rank component and a dictionary sparse component, wherein the dictionary consists of the a priori known characteristic spectral responses of the target we wish to localize. Finally, we analyze the performance of the proposed approach via experimental validation on real HS data for a classification task, and compare it with related techniques.
|Original language||English (US)|
|Title of host publication||Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017|
|Editors||Michael B. Matthews|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Apr 10 2018|
|Event||51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States|
Duration: Oct 29 2017 → Nov 1 2017
|Name||Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017|
|Other||51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017|
|Period||10/29/17 → 11/1/17|
Bibliographical noteFunding Information:
V. ACKNOWLEDGEMENT The authors graciously acknowledge support from the DARPA YFA, Grant N66001-14-1-4047.
© 2017 IEEE.
- Hyperspectral imaging
- dictionary sparse
- remote sensing
- target localization