Change detection techniques

Abstract

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.

Abbreviations used in this paper

6S second simulation of the satellite signal in the solar spectrum

ANN artificial neural networks

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

AVHRR Advanced Very High Resolution Radiometer

AVIRIS Airborne Visible/Infrared Imaging Spectrometer

CVA change vector analysis

EM expectation–maximization algorithm

ERS-1 Earth Resource Satellite-1

ETM+ Enhanced Thematic Mapper Plus, Landsat 7 satellite image

GIS Geographical Information System

GS Gramm–Schmidt transformation

J-M distance Jeffries–Matusita distance

KT Kauth–Thomas transformation or tasselled cap transformation

LSMA linear spectral mixture analysis

LULC land use and land cover

MODIS Moderate Resolution Imaging Spectroradiometer

MSAVI Modified Soil Adjusted Vegetation Index

MSS Landsat Multi-Spectral Scanner image

NDMI Normalized Difference Moisture Index

NDVI Normalized Difference Vegetation Index

NOAA National Oceanic and Atmospheric Administration

PCA principal component analysis

RGB red, green and blue colour composite

RTB ratio of tree biomass to total aboveground biomass

SAR synthetic aperture radar

SAVI Soil Adjusted Vegetation Index

SPOT HRV Satellite Probatoire d'Observation de la Terre (SPOT) high resolution visible image

TM Thematic Mapper

VI Vegetation Index

Acknowledgments

The authors wish to thank the National Science Foundation (grants 95-21918 and 99-06826) and the National Aeronautics and Space Administration (grant N005-334) for their support. The authors also would like to thank the anonymous reviewers for their comments and suggestions to improve this paper.