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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 2 processing, SPOT5 PAN (a) area A1: 2005 image; (b) area A1: 2008 image; (c) area A1: CD Difference index; (d) area A1: CD Ratio index; (e) area A2: 2005 image; (f) area A2: 2008 image; (g) area A2: CD Difference index; (h) area A2: CD Ratio Index.
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f4-sensors-12-03528: Level 2 processing, SPOT5 PAN (a) area A1: 2005 image; (b) area A1: 2008 image; (c) area A1: CD Difference index; (d) area A1: CD Ratio index; (e) area A2: 2005 image; (f) area A2: 2008 image; (g) area A2: CD Difference index; (h) area A2: CD Ratio Index.

Mentions: For resolution level 2, where panchromatic images are used, the simple difference and the ratio are two appropriate change detection indices for this type of data. As it has been mentioned before, for this level, the study has been restricted to two working areas, which are shown along with their corresponding change detection indices in Figure 4. For this level, after a preliminary visual survey, almost no dissimilarities are observed between the difference and ratio CD indices. However, as it is pointed out in Figure 4(c,d) (area A1), high grey level variations are appreciated, which indeed represent real changes between the two dates Figure 1(a,b).


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 2 processing, SPOT5 PAN (a) area A1: 2005 image; (b) area A1: 2008 image; (c) area A1: CD Difference index; (d) area A1: CD Ratio index; (e) area A2: 2005 image; (f) area A2: 2008 image; (g) area A2: CD Difference index; (h) area A2: CD Ratio Index.
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-12-03528: Level 2 processing, SPOT5 PAN (a) area A1: 2005 image; (b) area A1: 2008 image; (c) area A1: CD Difference index; (d) area A1: CD Ratio index; (e) area A2: 2005 image; (f) area A2: 2008 image; (g) area A2: CD Difference index; (h) area A2: CD Ratio Index.
Mentions: For resolution level 2, where panchromatic images are used, the simple difference and the ratio are two appropriate change detection indices for this type of data. As it has been mentioned before, for this level, the study has been restricted to two working areas, which are shown along with their corresponding change detection indices in Figure 4. For this level, after a preliminary visual survey, almost no dissimilarities are observed between the difference and ratio CD indices. However, as it is pointed out in Figure 4(c,d) (area A1), high grey level variations are appreciated, which indeed represent real changes between the two dates Figure 1(a,b).

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.