Detection of minelike targets in heavily cluttered environments using the MNF transform and grayscale morphological image reconstruction

Ashish Banerji; John Ioannis Goutsias

doi:10.1117/12.241264

31 May 1996 Detection of minelike targets in heavily cluttered environments using the MNF transform and grayscale morphological image reconstruction

Ashish Banerji, John Ioannis Goutsias

Author Affiliations +

Proceedings Volume 2765, Detection and Remediation Technologies for Mines and Minelike Targets; (1996) https://doi.org/10.1117/12.241264
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

We consider the problem of detecting minelike targets, imaged by means of multispectral sensors, that have been heavily corrupted by clutter. An effective detection approach needs to take into consideration the high correlation that is often present among bands in multispectral images and be robust against clutter. To this end, we here propose a two-step target detection approach. In particular, we first employ the Maximum Noise Fraction transform, in conjunction with vector-morphology, in order to reduce the effect of clutter and enhance the presence of targets. We then discuss a target detection algorithm, based on a morphological image reconstruction/marker fusion approach. We apply this algorithm to the problem of detecting minelike targets present in six-band aerial images, provided to us by the Coastal Systems Station, Naval Surface Warfare Center, Panama City, Florida. The proposed technique is relatively simple and requires only approximate knowledge of target size.

Citation Download Citation

Ashish Banerji and John Ioannis Goutsias "Detection of minelike targets in heavily cluttered environments using the MNF transform and grayscale morphological image reconstruction", Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); https://doi.org/10.1117/12.241264

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available