GeoCDX: An Automated Change Detection and Exploitation System for High-Resolution Satellite Imagery

We have developed a fully automated system for change detection of high-resolution satellite imagery. Our system, GeoCDX, is sensor-agnostic, resolution-independent and designed to process the very large volumes of data collected by modern high resolution panchromatic and multispectral imaging satellites. GeoCDX performs fully automated coregistration of imagery; extracts high-level features from the satellite imagery; performs change detection processing to pinpoint locations of change; clusters image tiles to group similar regions of change; and presents results in a variety of ways in an easy-to-use web application that facilitates online discovery, analysis, and dissemination of the change detection results. We applied GeoCDX to 4121 image pairs and successfully coregistered over 91% of the pairs covering a total area greater than 370 000 km2; GeoCDX decreased the average coregistration error from 9.6 ± 8.6 m to 1.8 ± 1.2 m. We show that for some pairs, GeoCDX provides up to a 50% increase in users' efficiency compared to manually performing change detection in common GIS software. Moreover, the change detection assessment performed using GeoCDX was on average four times more accurate compared to the manual approach in large part due to the use of our change intensity map that provides visual cues to the user during exploitation. Finally, change detection analysis using GeoCDX resulted in a missed detection rate of less than 2%.

[1]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Fabio Pacifici,et al.  Unsupervised change detection frameworks for very high spatial resolution images , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[3]  Jack Bresenham,et al.  Algorithm for computer control of a digital plotter , 1965, IBM Syst. J..

[4]  Curt H. Davis,et al.  A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[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]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[7]  Mihai Datcu,et al.  A Similarity Metric for Retrieval of Compressed Objects: Application for Mining Satellite Image Time Series , 2008, IEEE Transactions on Knowledge and Data Engineering.

[8]  Kidiyo Kpalma,et al.  An automatic image registration for applications in remote sensing , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Mihai Datcu,et al.  System Design Considerations for Image Information Mining in Large Archives , 2010, IEEE Geoscience and Remote Sensing Letters.

[10]  William J. Emery,et al.  An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[11]  N. Bigdely-Shamlo,et al.  Brain Activity-Based Image Classification From Rapid Serial Visual Presentation , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Siamak Khorram,et al.  The effects of image misregistration on the accuracy of remotely sensed change detection , 1998, IEEE Trans. Geosci. Remote. Sens..

[13]  Misha Pavel,et al.  Neurotechnology for Image Analysis: Searching For Needles in Haystacks Efficiently , 2007 .

[14]  Surya S. Durbha,et al.  Semantics-enabled framework for knowledge discovery from Earth observation data archives , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Mihai Datcu,et al.  Image Time-Series Data Mining Based on the Information-Bottleneck Principle , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Curt H. Davis,et al.  Pixel-Based Invariant Feature Extraction and its Application to Radiometric Co-Registration for Multi-Temporal High-Resolution Satellite Imagery , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Farid Melgani,et al.  Unsupervised Change Detection in Multispectral Remotely Sensed Imagery With Level Set Methods , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Turgay Çelik,et al.  Multiscale Change Detection in Multitemporal Satellite Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[19]  Ozy Sjahputera,et al.  Clustering of Detected Changes in High-Resolution Satellite Imagery Using a Stabilized Competitive Agglomeration Algorithm , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Ozy Sjahputera,et al.  GeoCDX: An Automated Change Detection & Exploitation System for High Resolution Satelite Imagery , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[21]  Martino Pesaresi,et al.  Systematic Study of the Urban Postconflict Change Classification Performance Using Spectral and Structural Features in a Support Vector Machine , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Francesca Bovolo,et al.  A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Chi-Ren Shyu,et al.  Entropy-Balanced Bitmap Tree for Shape-Based Object Retrieval From Large-Scale Satellite Imagery Databases , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[24]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[25]  David Lochbaum.  U.S. House of Representatives Committee on Science, Space, and Technology Energy & Environment and Investigations & Oversight Subcommittees , 2011 .

[26]  Lorenzo Bruzzone,et al.  Distributed Geospatial Data Processing Functionality to Support Collaborative and Rapid Emergency Response , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  C. Tao,et al.  A Comprehensive Study of the Rational Function Model for Photogrammetric Processing , 2001 .

[28]  J. Tasic,et al.  Colour spaces: perceptual, historical and applicational background , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[29]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[30]  Mihai Datcu,et al.  Human-centered concepts for exploration and understanding of Earth observation images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Francesca Bovolo,et al.  Analysis of the Effects of Pansharpening in Change Detection on VHR Images , 2010, IEEE Geoscience and Remote Sensing Letters.

[32]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[33]  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.

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

[35]  Francesca Bovolo,et al.  A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Palma Blonda,et al.  Automatic Spectral-Rule-Based Preliminary Classification of Radiometrically Calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 Imagery—Part I: System Design and Implementation , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Chi-Ren Shyu,et al.  GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries , 2007, IEEE Transactions on Geoscience and Remote Sensing.