Change detection for high-resolution remote sensing imagery using object-oriented change vector analysis method

Change Vector Analysis (CVA) is an important change detection method for remote sensing imagery with medium and low resolution. Traditional CVA is a pixel-based method, which is insufficient for high-resolution (HR) imagery. An object-oriented change vector analysis method (OCVA) is proposed in the paper. Image segmentation method was used to get image objects. The object histogtam was extracted as the feature of the image object at different bands. G statistic was employed to measure the distance between the object histograms at two times. Change vector of the object was built by the histogram distance at different bands. The heterogeneity of the object was measured by the magnitude of the object change vector. The threshold to detect change/no change objects was decided by Otsu method which maximizes the variance between change objects and unchanged objects. The experimental results on QuickBird images show that OCVA can make full use of the spatial information in HR images and get higher accuracy than pixel-based CVA.

[1]  Francesca Bovolo,et al.  Supervised change detection in VHR images using contextual information and support vector machines , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Cedric Nishan Canagarajah,et al.  Image segmentation using a texture gradient based watershed transform , 2003, IEEE Trans. Image Process..

[3]  Charalambos Kontoes,et al.  Operational land cover change detection using change vector analysis , 2008 .

[4]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[5]  Ashish Ghosh,et al.  Histogram thresholding for unsupervised change detection of remote sensing images , 2011 .

[6]  D. F. Prieto,et al.  An adaptive parcel-based technique for unsupervised change detection , 2000 .

[7]  Lorenzo Bruzzone,et al.  ICA and kernel ICA for change detection in multispectral remote sensing images , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[8]  Matti Pietikäinen,et al.  Unsupervised texture segmentation using feature distributions , 1997, Pattern Recognit..

[9]  Jun Chen,et al.  Change Vector Analysis in Posterior Probability Space: A New Method for Land Cover Change Detection , 2011, IEEE Geoscience and Remote Sensing Letters.

[10]  Arko Lucieer,et al.  Segmentation of multispectral high-resolution satellite imagery based on integrated feature distributions , 2010 .

[11]  Yuqi Tang,et al.  Object-oriented change detection based on the Kolmogorov–Smirnov test using high-resolution multispectral imagery , 2011 .

[12]  Guifeng Zhang,et al.  An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation , 2010, IEEE Transactions on Image Processing.

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

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