Unsupervised change detection in very high spatial resolution COSMO-Skymed SAR images

In this work we propose two pixel-wise change detection techniques for unsupervised network infrastructure monitoring in SAR imagery applications. The first algorithm is inspired by a well known algorithm, named RX, proposed to deal with anomaly detection in optical images. The second algorithm is a statistical based procedure, which exploits a nonparametric approach for estimating the probability density function of the image pair. In order to test and validate the proposed methods, we analyze a spot light amplitude COSMO-SkyMed image pair at one-meter spatial resolution acquired on a complex urban scenario. Experimental results obtained on the available dataset are presented and discussed.

[1]  Yifang Ban,et al.  Multitemporal Spaceborne SAR Data for Urban Change Detection in China , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..

[3]  Qinghua Li,et al.  High resolution SAR change detection in Hong Kong , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Dirk P. Kroese,et al.  Kernel density estimation via diffusion , 2010, 1011.2602.

[5]  Gabriele Moser,et al.  Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery , 2006, IEEE Trans. Geosci. Remote. Sens..

[6]  Bo Zhang,et al.  Change detection and analysis with radarsat-1 SAR image , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Lorenzo Bruzzone,et al.  An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Marco Diani,et al.  Detection of small changes in airborne hyperspectral imagery: Experimental results over urban areas , 2011, 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp).