NDLI: Reducing false alarms in automated target recognition using local sea-floor characteristics

Content Provider	IEEE Xplore Digital Library
Author	Daniell, O. Petillot, Y. Reed, S. Vazquez, J. Frau, A.
Copyright Year	2014
Description	Author affiliation: Oceans Syst. Lab., SeeByte Ltd., Heriot Watt Univ., Edinburgh, UK (Petillot, Y.) \|\| Ind. Doctorate Centre, Heriot Watt Univ., Edinburgh, UK (Daniell, O.) \|\| SeeByte Ltd., Edinburgh, UK (Reed, S.; Vazquez, J.; Frau, A.)
Abstract	This paper describes the use of local sea-floor characteristics to train a neural network to remove false alarms from an Automatic Target Recognition (ATR) algorithm. We demonstrate that this reduces the Probability of False Alarm (PFA) in difficult areas without impacting the Probability of Detection (PD) in flat areas. The sea-floor characteristics are calculated from the texture and appearance of clutter on the seafloor. Textural characteristics are extracted using a Dual Tree Wavelet (DTW) transform. Highlight and shadow regions are segmented using Markov Random Field (MRF) and graph cuts. Clutter density and height are calculated from the segmented image. The method is tested by training a neural network to filter the detections from a Haar cascade ATR algorithm. The neural network is trained on the ATR response and the seafloor characteristics. On Synthetic Aperture Sonar (SAS) data we report an average reduction of 50% in the false alarm rate over that of the ATR algorithm. The processing time for an 8000×3000 pixel image is approximately 1 second.
Starting Page	1
Ending Page	5
File Size	4306604
Page Count	5
File Format	PDF
ISBN	9781479952946
DOI	10.1109/SSPD.2014.6943308
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-08
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Anisotropic magnetoresistance Neural networks Approximation algorithms Synthetic aperture sonar Clutter
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automated target recognition with SAS: Shadow and highlight-based classification

Unified Design of a Feature-Based ADAC System for Mine Hunting Using Synthetic Aperture Sonar

Target classification from HR sonar images

Buried target detection with a synthetic aperture sonar

Automated cable detection in sonar imagery

Active contours for synthetic aperture sonar snippet registration

Knowledge-based segmentation of SAR images

SAR Segmentation and Recognition Based SCM

Reconstruction of undersampled SAS data using the WIPE algorithm

Reducing false alarms in automated target recognition using local sea-floor characteristics

Similar Documents

Automated target recognition with SAS: Shadow and highlight-based classification

Unified Design of a Feature-Based ADAC System for Mine Hunting Using Synthetic Aperture Sonar

Target classification from HR sonar images

Buried target detection with a synthetic aperture sonar

Automated cable detection in sonar imagery

Active contours for synthetic aperture sonar snippet registration

Knowledge-based segmentation of SAR images

SAR Segmentation and Recognition Based SCM

Reconstruction of undersampled SAS data using the WIPE algorithm

Reducing false alarms in automated target recognition using local sea-floor characteristics