DARPA/ONR ATR Activities

This page provides information regarding ATR effort under a DARPA/ONR contract. The group includes Raphi Coifman from Yale, Quyen Huynh from CSS, Nathan Intrator (P.I.) from Brown and Tel-Aviv University and Trung Nguyen from Boston University.

Slides

One page summary of our program
Darpa meeting Jun 23, 1998, Washington

Content

Side Scan Sonar Mine Detection
Back Scatter Sonar
Recent relevant publications

Related sites

June_98 Contractors meeting
Fast Mathematical Algorithms
Signal Processing workshop (Feb 9-11)
CSS Panama City  Publications
CIS Washington University
ONR  ONR/University 


Side Scan Sonar Mine Detection

The current data base consists of a 60-image set from a side-scan sonar (SSS0) collected at the Naval Surface Warfare Center (NSWC). They are encoded as 8-bit gray scale images, 1024 range cells by 511 cross-range cells. The 60 images contain 33 targets; some contain more than one target while others contain no targets. Non-target objects which look as targets appear throughout the images. A typical mine-like target consists of a strong highlight on its left side and a long shadow down range on its right side. Unfortunately the presence of clutter can mask this structure.

A preliminary report describes some effort in wavelet denoising of these images.
To optain the side-scan sonar or the backscatter data please contact Dr. Quyn Huynh 850-234-4158, or Dr. Edward Linsenmeyer 850-234-4161.

The following images represent the difficulty of the problem. The title includes the coordinates of the mine location (x is the first coordinate).

si000206 (Easy - 427,143; 860,351) si007225 (Difficult - 360,227)
si005134 (Difficult - 771,136) si001104 (Very Difficult - 307,254)

The three difficult images including a brief description, can be downloaded in MATLAB format (2MB).

Back Scatter Sonar

This application involves an active backscatter data set of mine-like objects. The data was collected at the Naval Surface Warfare Center (NSWC) by Gerald Dobeck. The task is to distinguish between man-made and non-man-made objects. There are six objects in the data: metal cylinder, cone-shaped plastic object, water-filled barrel, limestone rock, granite rock, and a water-logged wooden log. The data-set contained seven different frequency bands, however in this preliminary study, only one frequency band, an FM sweep between 20 to 60 kHz was used. The targets were suspended in a large water tank, while cylindrical objects were suspended horizontally. Measurements were collected in 5 degree increments on a rotating target around a vertical axis. Every second measurement was used for testing, thus the train and test data were interleaved and both included measurements at 10 degree increments.

A preliminary report appeard in SPIE-97. It describes some effort in wavelet denoising of these images.

Recent Relevant Publications

  • Quyen Q. Huynh, Leon N Cooper, Nathan Intrator and Harel Shouval.  Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory To appear: IEEE Transactions On Signal Processing, Special issue on NN
  • N. Intrator, Q. Q. Huynh, Y. S. Wong, B. H. K. Lee.  Wavelet Feature Extraction for Discrimination TasksTo appear: Proceedings of The 1997 Canadian Workshop on Information Theory, Toronto June 3-4, 97
  • N. Intrator, Q. Q. Huynh and G. Dobeck.  Feature extraction from acoustic backscattered signals using wavelet dictionariesProceedings of SPIE97, April, 1997
  • Q. Q. Huynh, N. Neretti, N. Intrator and G. Dobeck.  Denoising of sonar imageryPreliminary Report (February, 1998)
  • Q. Q. Huynh, N. Neretti, N. Intrator and G. Dobeck.  Image enhancement for pattern recognitionProceedings of SPIE98, April, 1998