A Reliability-Based Multi-Algorithm Fusion Technique in Detecting Changes in Land Cover

Detecting land use or land cover changes is a challenging problem in analyzing images. Change-detection plays a fundamental role in most of land use or cover monitoring systems using remote-sensing techniques. The reliability of individual automatic change-detection algorithms is currently below operating requirements when considering the intrinsic uncertainty of a change-detection algorithm and the complexity of detecting changes in remote-sensing images. In particular, most of these algorithms are only suited for a specific image data source, study area and research purpose. Only a number of comprehensive change-detection methods that consider the reliability of the algorithm in different implementation situations have been reported. This study attempts to explore the advantages of combining several typical change-detection algorithms. This combination is specifically designed for a highly reliable change-detection task. Specifically, a fusion approach based on reliability is proposed for an exclusive land use or land cover change-detection. First, the reliability of each candidate algorithm is evaluated. Then, a fuzzy comprehensive evaluation is used to generate a reliable change-detection approach. This evaluation is a transformation between a one-way evaluation matrix and a weight vector computed using the reliability of each candidate algorithm. Experimental results reveal that the advantages of combining these distinct change-detection techniques are evident.

[1]  Pieter Kempeneers,et al.  Increasing Robustness of Postclassification Change Detection Using Time Series of Land Cover Maps , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Jianya Gong,et al.  An integrated spatio-temporal classification method for urban fringe change detection analysis , 2012 .

[3]  Barbara Koch,et al.  Change vector analysis to categorise land cover change processes using the tasselled cap as biophysical indicator , 2008, Environmental monitoring and assessment.

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

[5]  R. W. A. Barnard Reliability Engineering : Futility and Error , 2005 .

[6]  Allan Aasbjerg Nielsen,et al.  Kernel Maximum Autocorrelation Factor and Minimum Noise Fraction Transformations , 2011, IEEE Transactions on Image Processing.

[7]  LI X.,et al.  Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta , 2001 .

[8]  Kai-Kuang Ma,et al.  Unsupervised Change Detection for Satellite Images Using Dual-Tree Complex Wavelet Transform , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[9]  J S Osberg,et al.  Kappa coefficient calculation using multiple ratings per subject: a special communication. , 1989, Physical therapy.

[10]  Elnour Elfaki,et al.  DETECTION OF LAND COVER CHANGES USING MULTI-TEMPORAL SATELLITE IMAGERY , 2015 .

[11]  I. Niemeyer,et al.  DETECTION OF LAND COVER CHANGES USING MULTI-TEMPORAL SATELLITE IMAGERY , 2008 .

[12]  Guomo Zhou,et al.  Monitoring the change of urban wetland using high spatial resolution remote sensing data , 2010 .

[13]  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.

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

[15]  Juha Hyyppä,et al.  Automatic Detection of Buildings and Changes in Buildings for Updating of Maps , 2010, Remote. Sens..

[16]  Y. Shimabukuro,et al.  Change vector analysis technique to monitor selective logging activities in Amazon , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[17]  Duk-jin Kim,et al.  Characterization of Arctic Sea Ice Thickness Using High-Resolution Spaceborne Polarimetric SAR Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Dawei Han,et al.  Selection of classification techniques for land use - land cover change investigation , 2012 .

[19]  D. Roberts,et al.  Spectral shape-based temporal compositing algorithms for MODIS surface reflectance data , 2007 .

[20]  Beáta Csathó,et al.  A New Methodology for Detecting Ice Sheet Surface Elevation Changes From Laser Altimetry Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Jinsong Deng,et al.  PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data , 2008 .

[22]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[23]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[24]  Michael J. Falkowski,et al.  Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests , 2012 .

[25]  P. Gong,et al.  Land-use/land-cover change detection using improved change-vector analysis , 2003 .

[26]  Priyakant Sinha,et al.  Binary images in seasonal land-cover change identification: a comparative study in parts of New South Wales, Australia , 2013 .

[27]  Peijun Shi,et al.  Detecting land-use/land-cover change in rural-urban fringe areas using extended change-vector analysis , 2011, Int. J. Appl. Earth Obs. Geoinformation.

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

[29]  Knut Conradsen,et al.  Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies , 1998 .

[30]  Sarnam Singh,et al.  Forest cover change detection of Western Ghats of Maharashtra using satellite remote sensing based visual interpretation technique. , 2010 .

[31]  Timothy A. Warner,et al.  Change Detection Accuracy and Image Properties: A Study Using Simulated Data , 2010, Remote. Sens..

[32]  Tom Milliman,et al.  Detection of Large-Scale Forest Canopy Change in Pan-Tropical Humid Forests 2000–2009 With the SeaWinds Ku-Band Scatterometer , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[33]  R. Lunetta,et al.  Land-cover change detection using multi-temporal MODIS NDVI data , 2006 .