Fault-Tolerant Building Change Detection From Urban High-Resolution Remote Sensing Imagery

This letter proposes a novel change detection model, focusing on building change information extraction from urban high-resolution imagery. It consists of two blocks: 1) building interest-point detection, using the morphological building index (MBI) and the Harris detector; and 2) multitemporal building interest-point matching and the fault-tolerant change detection. The proposed method is insensitive to the geometrical differences of buildings caused by different imaging conditions in the multitemporal high-resolution imagery and is able to significantly reduce false alarms. Experiments showed that the proposed method was effective for building change detection from multitemporal urban high-resolution images. Moreover, the effectiveness of the algorithm was validated by comparing with the morphological change vector analysis (CVA), parcel-based CVA, and MBI-based CVA.

[1]  Francesca Bovolo,et al.  A Context-Sensitive Technique Robust to Registration Noise for Change Detection in VHR Multispectral Images , 2010, IEEE Transactions on Image Processing.

[2]  Mattia Marconcini,et al.  A Novel Partially Supervised Approach to Targeted Change Detection , 2011, IEEE Transactions on Geoscience and Remote Sensing.

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

[4]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[5]  Fabio Del Frate,et al.  Automatic Change Detection in Very High Resolution Images With Pulse-Coupled Neural Networks , 2010, IEEE Geoscience and Remote Sensing Letters.

[6]  Xin Huang,et al.  A Multidirectional and Multiscale Morphological Index for Automatic Building Extraction from Multispectral GeoEye-1 Imagery , 2011 .

[7]  Jon Atli Benediktsson,et al.  An Unsupervised Technique Based on Morphological Filters for Change Detection in Very High Resolution Images , 2008, IEEE Geoscience and Remote Sensing Letters.

[8]  Francesca Bovolo,et al.  A Multilevel Parcel-Based Approach to Change Detection in Very High Resolution Multitemporal Images , 2005, IEEE Geoscience and Remote Sensing Letters.

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

[10]  Francesca Bovolo,et al.  Analysis and Adaptive Estimation of the Registration Noise Distribution in Multitemporal VHR Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Liangpei Zhang,et al.  Morphological Building/Shadow Index for Building Extraction From High-Resolution Imagery Over Urban Areas , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Pierre Soille,et al.  Change Detection Based on Information Measure , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

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

[15]  Yun Zhang,et al.  A Novel Interest-Point-Matching Algorithm for High-Resolution Satellite Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.