Change detection techniques

Dengsheng Lu

doi:10.1080/0143116031000139863

2010-06-03

2.4K

PRE

DOI：10.1080/0143116031000139863

原文链接

原文求助

摘要

En 中文

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

Keyword:

LAND-COVER CHANGE

CHANGE-VECTOR ANALYSIS

DIGITAL CHANGE DETECTION

PRINCIPAL COMPONENTS-ANALYSIS

WETLAND CHANGE DETECTION

THEMATIC MAPPER DATA

URBAN CHANGE DETECTION

SOUTH-WESTERN NIGERIA

REMOTE-SENSING DATA

PEARL RIVER DELTA