Geographic Object-based Image Change Analysis

Remote sensing is an effective tool for mapping earth surface objects and phenomena, and provides the sole means for comprehensive monitoring of land surface changes. Normally captured in an image form by sensors mounted on aircraft or satellites, remotely sensed data are spatially contiguous and temporally periodic measurements of the reflected or emitted electromagnetic radiation (EMR) leaving the earth’s surface. In order to create or update maps of earth surface objects or phenomena, these image data must be visually interpreted by humans and/or processed by computer routines. For the past 30 years, a major emphasis has been placed on computer-assisted approaches to mapping and monitoring earth surface objects and phenomena, to achieve greater efficiency and objectivity, which are inherent to such approaches.

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

[2]  J. Rogan,et al.  Remote sensing technology for mapping and monitoring land-cover and land-use change , 2004 .

[3]  J. Cihlar,et al.  Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection , 2002 .

[4]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[5]  Curtis E. Woodcock,et al.  Nested-hierarchical scene models and image segmentation , 1992 .

[6]  John R. Jensen,et al.  A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .

[7]  G. J. Hay,et al.  A multiscale framework for landscape analysis: Object-specific analysis and upscaling , 2001, Landscape Ecology.

[8]  Philippe De Maeyer,et al.  Object-oriented change detection for the city of Harare, Zimbabwe , 2009, Expert Syst. Appl..

[9]  M. Neubert,et al.  Assessing image segmentation quality – concepts, methods and application , 2008 .

[10]  Martin Kappas,et al.  Remote Sensing & GIS for Environmental Studies: Applications in Geography , 2005 .

[11]  Philippe De Maeyer,et al.  An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization , 2007, Expert Syst. Appl..

[12]  Patrick Bogaert,et al.  Forest change detection by statistical object-based method , 2006 .

[13]  Suming Jin,et al.  Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances , 2005 .

[14]  P. Mayaux,et al.  An object-based method for mapping and change analysis in mangrove ecosystems , 2008 .

[15]  D. Stow,et al.  Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery , 2008 .

[16]  Patrick Bogaert,et al.  Tropical forest monitoring by object-based change detection : towards an automated method in an operational perspective , 2006 .

[17]  G. Hay,et al.  Object-Based Image Analysis , 2008 .

[18]  S. Sader,et al.  Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series , 2001 .

[19]  A. Yeh,et al.  Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta , 1998 .

[20]  T. Blaschke.  TOWARDS A FRAMEWORK FOR CHANGE DETECTION BASED ON IMAGE OBJECTS , 2005 .

[21]  J. Morisette,et al.  Accuracy Assessment Curves for Satellite-Based Change Detection , 2000 .

[22]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[23]  D. Stow.  Review Article Remotely-sensed tracers for hydrodynamic surface flow estimation , 1987 .

[24]  Alan H. Strahler,et al.  On the nature of models in remote sensing , 1986 .

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

[26]  C. Woodcock,et al.  Combining Spectral and Texture Data in the Segmentation of Remotely Sensed Images , 1996 .

[27]  William J. Volchok,et al.  Radiometric scene normalization using pseudoinvariant features , 1988 .

[28]  Xiaojun Yang,et al.  Relative Radiometric Normalization Performance for Change Detection from Multi-Date Satellite Images , 2000 .

[29]  Volker Walter,et al.  Object-based classification of remote sensing data for change detection , 2004 .

[30]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[31]  Douglas A. Stow,et al.  Sensitivity of multitemporal NOAA AVHRR data of an urbanizing region to land-use/land-cover changes and misregistration , 2002 .

[32]  A. Rango,et al.  Combining Decision Trees with Hierarchical Object-oriented Image Analysis for Mapping Arid Rangelands , 2007 .

[33]  John R. Jensen,et al.  Object‐based change detection using correlation image analysis and image segmentation , 2008 .

[34]  K. K. Mayo,et al.  Monitoring land-cover change by principal component analysis of multitemporal Landsat data. , 1980 .

[35]  Douglas J. King,et al.  Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration , 2002 .

[36]  Volker Walter.  AUTOMATIC CLASSIFICATION OF REMOTE SENSING DATA FOR GIS DATABASE REVISION , 2003 .

[37]  Kelley A. Crews-Meyer,et al.  Characterizing Landscape Dynamism Using Paneled-Pattern Metrics , 2002 .

[38]  Geoffrey J. Hay,et al.  Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline , 2008 .

[39]  R. G. Pontius.  Statistical Methods to Partition Effects of Quantity and Location During Comparison of Categorical Maps at Multiple Resolutions , 2002 .

[40]  C. Burnett,et al.  A multi-scale segmentation/object relationship modelling methodology for landscape analysis , 2003 .

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

[42]  Lloyd L. Coulter,et al.  Monitoring cross-border trails using airborne digital multispectral imagery and interactive image analysis techniques , 2007 .

[43]  D. Stow.  Reducing the effects of misregistration on pixel-level change detection , 1999 .

[44]  R. Kettig,et al.  Classification of Multispectral Image Data by Extraction and Classification of Homogeneous Objects , 1976, IEEE Transactions on Geoscience Electronics.

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

[46]  Peng Gong,et al.  Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery , 2004 .

[47]  Ram M. Narayanan,et al.  A shape-based approach to change detection of lakes using time series remote sensing images , 2003, IEEE Trans. Geosci. Remote. Sens..

[48]  Agustin Lobo,et al.  Image segmentation and discriminant analysis for the identification of land cover units in ecology , 1997, IEEE Trans. Geosci. Remote. Sens..

[49]  Austin Troy,et al.  Object-based Land Cover Classification and Change Analysis in the Baltimore Metropolitan Area Using Multitemporal High Resolution Remote Sensing Data , 2008, Sensors.

[50]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[51]  Douglas A. Stow,et al.  Evaluation of Remote Sensing Techniques for Mapping Transborder Trails , 2004 .

[52]  G. Hay,et al.  A Multiscale Object-Specific Approach to Digital Change Detection , 2003 .

[53]  Patrick Bogaert,et al.  An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution , 2008 .

[54]  Mathias Schardt,et al.  Single tree detection in very high resolution remote sensing data , 2007 .

[55]  T. Warner,et al.  Multitemporal censusing of a population of eastern hemlock (Tsuga canadensis L.) from remotely sensed imagery using an automated segmentation and reconciliation procedure , 2005 .

[56]  R. Lunetta,et al.  Remote Sensing Change Detection: Environmental Monitoring Methods and Applications , 1999 .

[57]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[58]  J. Franklin,et al.  Testing the Woodcock-Harward image segmentation algorithm in an area of southern California chaparral and woodland vegetation , 1996 .

[59]  Linda G. Shapiro,et al.  Computer Vision , 2001 .