Change detection algorithm for multi-temporal satellite images: a review

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Change detection (CD) is the process of detecting changes from multitemporal satellite images that have undergone spatial changes due to natural and man-made disaster. The objective is to analyse different change detection techniques, in order to use appropriately in various applications with the help of image processing. Techniques that are used in current researches are Image Differencing, Image Regression, Change Vector Analysis (CVA),Principal Component Analysis(PCA), Tasselled Cap, Gramm-Schmidt(GS), Post Classification Comparison, EM Detection, Unsupervised Change Detection, Li-Strahler Reflectance Model, Spectral Mixture Model, Biophysical Parameter Method, Integrated GIS and Remote Sensing Method, GIS Approach, Visual Interpretation and so on. Effective change detection is required for various applications such as rate of deforestation, costal changes, urban developments, damage evaluation, resource monitoring and land disposition.

     

     

  • Keywords


    Change detection, principal component analysis, remote sensing

  • References


      [1] Coppin P, Jonckheere I, Nackaerts K, Muys B & Lambin E, “Digital Change Detection Methods in Ecosystem Monitoring”, International Journal of Remote Sensing, (2010).

      [2] Alqurashi AF & Kumar L, “Land use and land cover change detection in the Saudi Arabian desert cities of Makkah and Al-Taif using satellite data”, Advances in Remote Sensing, Vol.3, No.3, (2014), pp.106-119.

      [3] Im J & Jensen JR, “A change detection model based on neighborhood correlation image analysis and decision tree classification”, Remote Sensing of Environment, Vol.99, No.3, (2005), pp.326-340.

      [4] Shetty A & Minu S, “A Comparative Study of Image ChangeDetection Algorithms in MATLAB”, International Conference on Water Resources, Coastal and Ocean Engineering, (2015).

      [5] Al-doski J, Mansor SB & Shafri HZM, “Change Detection Process and Techniques”, The International Institute for Science, Technology and Education (IISTE), (2013).

      [6] Singh A, “Digital Change Detection Techniques Using Remotely-Sensed Data”, International Journal of Remote Sensing, (2010).

      [7] Shahabi H & Ahmad BB, “Detection of urban and green space destruction using Normalized DifferenceVegetation Index (NDVI), Principal Component Analysis (PCA) and postclassification methods: A case study of Saqqez city”, International Journal of the Physical Sciences, (2012).

      [8] Lambin EF & Strahlers AH, “Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data”, Remote sensing of environment, Vol.48, No.2,(2014), pp.231-244.

      [9] Crist EP & Cicone RC, “A Physically-Based Transformation of Thematic Mapper Data-The TM Tasselled Cap”, IEEE Transactions on Geoscience and Remote Sensing, (1984).

      [10] Lu D, Mausel P, Brondizio E & Moran E, “Change Detection Techniques”, International Journal of Remote Sensing, (2004).

      [11] Li X & Yeh AGO, “Principal Component Analysis of Stacked Multi-Temporal Images for the Monitoring of Rapid Urban Expansion in the Pearl River Delta”, International Journal of Remote Sensing, (2010).

      [12] Serra P, Pons X & D Sauri, “Post-Classification Change Detection with Data from Different Sensors: Some Accuracy Considerations”, International Journal of Remote Sensing, (2013).

      [13] Afify HA, “Evaluation Of Change Detection Techniques ForMonitoring Land-Cover Changes: A Case Study In New Burj AI-ArabArea”, Alexandria Engineering Journal, (2011).

      [14] Bruzzone L & Prieto DF, “Automatic analysis of the difference image for unsupervised change detection”, IEEE Transactions on Geoscience and Remote sensing, Vol.38, No.3,(2000), pp.1171-1182.

      [15] Salih AA, Ganawa ET & Elmahl AA, “Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery”, The Egyptian Journal of Remote Sensing and Space Science, Vol.20, (2017), pp.S21-S29.

      [16] Blaschke T, Lang S, Lorup E, Strobl J & Zeil P, “Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications”, Environmental information for planning, politics and the public, Vol.2, (2000), pp.555-570.

      [17] Lo CP & Shipman RL, “A GIS approach to land-use change dynamics detection”, PE&RS, Photogrammetric Engineering & Remote Sensing, Vol.56, No.1,(1990), pp.1483-1491.

      [18] Puig CJ, Hyman G & Bolaños S, “Digital classification vs visual interpretation: a case study in humid tropical forests of the Peruvian Amazon”, International Center for Tropical Agriculture, (2002).


 

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Article ID: 12173
 
DOI: 10.14419/ijet.v7i2.21.12173




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