RASCAN type radar image resolution enhancement for non-metallic landmine detection

Andres Quintero-Zea; Marisol Osorio-Cardenas

doi:10.1109/STSIVA.2013.6644923

RASCAN type radar image resolution enhancement for non-metallic landmine detection

Andres Quintero-Zea, Marisol Osorio-Cardenas

Universidad de Medellín

Research output: Contribution to conference › Paper

2 Scopus citations

Abstract

Category 3. This paper presents two resolution enhancement techniques applied to images of buried landmines acquired using a RASCAN radar, a holographic radar that is used mainly for sounding structural components of buildings, although it can be used to find buried objects too. The first technique is a phase-driven spatially variant regularization, and the second one is a wavelet-based image interpolation. © 2013 IEEE.

Original language	American English
DOIs	https://doi.org/10.1109/STSIVA.2013.6644923
State	Published - 31 Dec 2013
Event	Symposium of Signals, Images and Artificial Vision - 2013, STSIVA 2013 - Duration: 31 Dec 2013 → …

Conference

Conference	Symposium of Signals, Images and Artificial Vision - 2013, STSIVA 2013
Period	31/12/13 → …

Access to Document

10.1109/STSIVA.2013.6644923

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title = "RASCAN type radar image resolution enhancement for non-metallic landmine detection",

abstract = "Category 3. This paper presents two resolution enhancement techniques applied to images of buried landmines acquired using a RASCAN radar, a holographic radar that is used mainly for sounding structural components of buildings, although it can be used to find buried objects too. The first technique is a phase-driven spatially variant regularization, and the second one is a wavelet-based image interpolation. {\textcopyright} 2013 IEEE.",

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AB - Category 3. This paper presents two resolution enhancement techniques applied to images of buried landmines acquired using a RASCAN radar, a holographic radar that is used mainly for sounding structural components of buildings, although it can be used to find buried objects too. The first technique is a phase-driven spatially variant regularization, and the second one is a wavelet-based image interpolation. © 2013 IEEE.

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DO - 10.1109/STSIVA.2013.6644923

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Y2 - 31 December 2013

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