✦ LIBER ✦

Multiscale Models for Target Detection and Background Discrimination in Synthetic Aperture Radar Imagery

✍ Scribed by David Howard; Jim Schroeder

Publisher: Elsevier Science
Year: 1999
Tongue: English
Weight: 96 KB
Volume: 9
Category: Article
ISSN: 1051-2004
DOI: 10.1006/dspr.1999.0338

No coin nor oath required. For personal study only.

✦ Synopsis

Multiscale models can be used to capture the scale-dependent behavior of the statistics in radar imagery. This behavior is expected to be different for natural background compared to objects of interest such as vehicles. We demonstrate that multiscale autoregressive models can discriminate between samples of these two major classes extracted from 1.5-mresolution radar imagery. We also show that it is possible to discriminate between two types of natural background in SAR imagery, ''grassland'' and ''woodland,'' using multiscale models. This latter result could be exploited in adaptive algorithms for automated target detection. 1999 Academic Press

📜 SIMILAR VOLUMES

Comparison of Selected Features for Targ

Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery

✍ Tristrom Cooke; Nicholas J. Redding; Jim Schroeder; Jingxin Zhang 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 148 KB

Several methods are available that capture the statistics of radar imagery. The best features, in the sense of man-made target discrimination, are expected to be different for different types of natural background and for different objects of interest such as vehicles. We demonstrate that discrimina

Multispectral and synthetic aperture rad

Multispectral and synthetic aperture radar remote-sensing-based models for holocene coastline development in the Ambracian Gulf, Epirus, Greece

✍ Benjamin R. Vining; James Wiseman 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 569 KB

An unsupervised vector quantization-base

An unsupervised vector quantization-based target subspace projection approach to mixed pixel detection and classification in unknown background for remotely sensed imagery

✍ Clark Brumbley; Chein-I Chang 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 874 KB

A recently developed orthogonal subspace projection (OSP) approach has been successfully applied to AVIRIS as well as HYDICE data for image classi"cation. However, it has found that OSP performs poorly in multispectral image classi"cation such as 3-band SPOT data. This is primarily due to the fact t