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Evaluation of a change detection methodology by means of binary thresholding algorithms and informational fusion processes.

Molina I, Martinez E, Arquero A, Pajares G, Sanchez J - Sensors (Basel) (2012)

Bottom Line: Those changes produce significant effects in human and natural activities.Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations.The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

View Article: PubMed Central - PubMed

Affiliation: ETSITGC, Universidad Polit├ęcnica de Madrid, Madrid, Spain. inigo.molina@upm.es

ABSTRACT
Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth's resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

No MeSH data available.


Level 1 processing, SPOT5 XS (a) 2005 normalized image; (b) 2008 image; (c) CD CVA index; (d) CD NDVI difference index.
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f3-sensors-12-03528: Level 1 processing, SPOT5 XS (a) 2005 normalized image; (b) 2008 image; (c) CD CVA index; (d) CD NDVI difference index.

Mentions: Thus, for the first dataset (resolution level 1) the CVA method and the NDVI difference index have been applied. For the first change detection index bands 1 (green), 2 (red) and 3 (near infrared) have been used. Whereas for the second change detection index only bands 2 and 3 are required. Figure 3 shows the normalized (earlier date) and the reference (later date) images, as well as the derived change detection indices for level 1.


Evaluation of a change detection methodology by means of binary thresholding algorithms and informational fusion processes.

Molina I, Martinez E, Arquero A, Pajares G, Sanchez J - Sensors (Basel) (2012)

Level 1 processing, SPOT5 XS (a) 2005 normalized image; (b) 2008 image; (c) CD CVA index; (d) CD NDVI difference index.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376593&req=5

f3-sensors-12-03528: Level 1 processing, SPOT5 XS (a) 2005 normalized image; (b) 2008 image; (c) CD CVA index; (d) CD NDVI difference index.
Mentions: Thus, for the first dataset (resolution level 1) the CVA method and the NDVI difference index have been applied. For the first change detection index bands 1 (green), 2 (red) and 3 (near infrared) have been used. Whereas for the second change detection index only bands 2 and 3 are required. Figure 3 shows the normalized (earlier date) and the reference (later date) images, as well as the derived change detection indices for level 1.

Bottom Line: Those changes produce significant effects in human and natural activities.Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations.The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

View Article: PubMed Central - PubMed

Affiliation: ETSITGC, Universidad Polit├ęcnica de Madrid, Madrid, Spain. inigo.molina@upm.es

ABSTRACT
Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth's resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

No MeSH data available.