A Sequential Framework for Image Change Detection

被引：7

作者：

Lingg, Andrew J. ^{[1
]}

Zelnio, Edmund ^{[2
]}

Garber, Fred ^{[1
]}

Rigling, Brian D. ^{[1
]}

机构：

[1] Wright State Univ, Dayton, OH 45435 USA

[2] US Air Force, Res Lab, Wright Patterson AFB, OH 45433 USA

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2014年 / 23卷 / 05期

关键词：

Image analysis; image sequence analysis; subtraction techniques; SYNTHETIC-APERTURE RADAR; UNSUPERVISED CHANGE DETECTION; AUTOMATIC CHANGE DETECTION; MODEL; ALGORITHMS; SEQUENCES;

D O I：

10.1109/TIP.2014.2309432

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a sequential framework for change detection. This framework allows us to use multiple images from reference and mission passes of a scene of interest in order to improve detection performance. It includes a change statistic that is easily updated when additional data becomes available. Detection performance using this statistic is predictable when the reference and image data are drawn from known distributions. We verify our performance prediction by simulation. Additionally, we show that detection performance improves with additional measurements on a set of synthetic aperture radar images and a set of visible images with unknown probability distributions.

引用

页码：2405 / 2413

页数：9

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