Change detection based on iterative invariant area histogram matching

Image radiometric distortion is problematic to many remote sensing based image change detection methods. Thus image radiometric normalization is critical to successful change detection and other image analysis. Histogram matching (HM) method has been widely used for radiometric normalization. However, when two corresponding image scenes have partial changes, these changes will introduce additional distortions in the histogram matching process. Obviously, if the change areas in the images can be excluded from histogram matching, the distortions in histogram matching will be avoided. Thus, a novel, iterative change detection based HM normalization algorithm is proposed in this paper. The algorithm of the proposed iterative invariant area histogram matching and an adaptive thresholding schema are first introduced, and the change detection experiment results are then presented. The preliminary results indicate that the proposed method significantly improves the performance of change detection for a given threshold as judged by visual inspection.

[1]  H. Schmidt,et al.  Multitemporal analysis of satellite data and their use in the monitoring of the environmental impacts of open cast lignite mining areas in Eastern Germany , 1998 .

[2]  S. P. S. Kushwaha,et al.  Evaluation of various digital image processing techniques for detection of coastal wetlands using ERS-1 SAR data , 2000 .

[3]  Zhengwei Yang,et al.  Heterogeneously sensed imagery radiometric response normalization for citrus grove change detection , 2007, SPIE Optics East.

[4]  J. C. Taylor,et al.  Monitoring landscape change in the National Parks of England and Wales using aerial photo interpretation and GIS , 2000 .

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

[6]  Philip J. Howarth,et al.  Procedures for change detection using Landsat digital data , 1981 .

[7]  P. E. Anuta,et al.  Change detection in coastal zone environments , 1977 .

[8]  S. Franklin,et al.  Large-area forest structure change detection: An example , 2002 .

[9]  Derya Maktav,et al.  Change detection of land cover and land use using remote sensing and GIS: a case study in Kemer, Turkey , 2009 .

[10]  Curtis E. Woodcock,et al.  Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors , 2001 .

[11]  Jinfei Wang,et al.  Monitoring the change of Phragmites distribution using satellite data , 2003 .

[12]  Richard G. Lathrop,et al.  Urban change detection based on an artificial neural network , 2002 .

[13]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[14]  Zhengwei Yang,et al.  SPATIAL-SPECTRAL CROSS-CORRELATION FOR CHANGE DETECTION ---A CASE STUDY FOR CITRUS COVERAGE CHANGE DETECTION , 2007 .

[15]  W. Malila.  Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat , 1980 .

[16]  C. Lo,et al.  Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area , 2002 .

[17]  J. Mas.  Monitoring land-cover changes: A comparison of change detection techniques , 1999 .

[18]  R. Nelson.  Detecting forest canopy change due to insect activity using Landsat MSS , 1983 .