An automated binary change detection model using a calibration approach

Abstract An automated binary change detection model using a threshold-based calibration approach was introduced in the study. The burdensome processes required in binary change detection, including calibration, calculation of accuracy, extraction of optimum threshold(s), generation of a binary change mask, and removal of “salt-and-pepper” noise were integrated and automated in the model. For practical purpose, the model was implemented as a dynamic linked library in ESRI ArcMap 9.1 using Visual Basic. This study demonstrated the model with a variety of single and multiple variables (layers) extracted from multiple-date QuickBird imagery for three study sites in Las Vegas, NV and two study sites in Tucson, AZ. The use of multiple variables in binary change detection resulted in significantly better performance than single variables.

[1]  S. Khorram,et al.  Remotely Sensed Change Detection Based on Artificial Neural Networks , 1999 .

[2]  M. Ridd,et al.  A Comparison of Four Algorithms for Change Detection in an Urban Environment , 1998 .

[3]  Tung Fung,et al.  The Determination of Optimal Threshold Levels for Change Detection Using Various Accuracy Indices , 2008 .

[4]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[5]  J. Chan,et al.  Detecting the nature of change in an urban environment : A comparison of machine learning algorithms , 2001 .

[6]  Peng Gong,et al.  Registration-noise reduction in difference images for change detection , 1992 .

[7]  M. Bauer,et al.  Digital change detection in forest ecosystems with remote sensing imagery , 1996 .

[8]  J. R. Jensen,et al.  An evaluation of the CoastWatch change detection protocol in South Carolina , 1993 .

[9]  R. Lunetta,et al.  Remote Sensing Change Detection: Environmental Monitoring Methods and Applications , 1999 .

[10]  John R. Jensen,et al.  Development of a remote sensing change detection system based on neighborhood correlation image analysis and intelligent knowledge-based systems , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[11]  G. Hay,et al.  A Multiscale Object-Specific Approach to Digital Change Detection , 2003 .

[12]  D. Lu,et al.  Change detection techniques , 2004 .

[13]  Dengsheng Lu,et al.  Land‐cover binary change detection methods for use in the moist tropical region of the Amazon: a comparative study , 2005 .

[14]  Kenneth C. McGwire,et al.  Integration of geographic information systems and remote sensing , 1997 .

[15]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[16]  Christopher Munyati,et al.  Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats, Zambia , 2004 .

[17]  R. D. Johnson,et al.  Change vector analysis: A technique for the multispectral monitoring of land cover and condition , 1998 .

[18]  Michael E. Hodgson,et al.  A Parameterization Model for Transportation Feature Extraction , 2004 .

[19]  John R. Jensen,et al.  A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .

[20]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[21]  C. Munyati,et al.  Wetland change detection on the Kafue Flats, Zambia, by classification of a multitemporal remote sensing image dataset , 2000 .

[22]  D. Civco,et al.  A COMPARISON OF LAND USE AND LAND COVER CHANGE DETECTION METHODS , 2002 .

[23]  J. Morisette,et al.  Accuracy Assessment Curves for Satellite-Based Change Detection , 2000 .

[24]  David M. Johnson,et al.  Impacts of vegetation dynamics on the identification of land-cover change in a biologically complex community in North Carolina, USA , 2002 .

[25]  S. Sader,et al.  Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series , 2001 .

[26]  R. Lunetta,et al.  A change detection experiment using vegetation indices. , 1998 .

[27]  K. Rutchey,et al.  Inland Wetland Change Detection in the Everglades Water Conservation Area 2A Using a Time Series of Normalized Remotely Sensed Data , 1995 .