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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 Politécnica 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.


Summative method (Σ) rose plots (a) Level 1-case 7 (Table 4); (b) Level 2-case 7 (Table 5); (c) Level 3.
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f12-sensors-12-03528: Summative method (Σ) rose plots (a) Level 1-case 7 (Table 4); (b) Level 2-case 7 (Table 5); (c) Level 3.

Mentions: The last part of this section describes the graphical representation of the quality measures for the three levels of resolution. For the summative method, only the best results (cases 7–l1, 7–l2 and 3–l4) achieved for each level are shown in Figure 12. For this purpose rose plots [51] have been used. These diagrams are constructed from the information contained in confusions matrices, their different sectors indicate the percentages of true and false positives, as well as the corresponding negatives. The high proportions of true positives and negatives vs. the lower proportions of false negatives reveal the good performance of this informational fusion method for levels 1 and 2 for the given cases. This tendency changes for level 3, where the sectors corresponding to the false positives and negatives increase noticeably.


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)

Summative method (Σ) rose plots (a) Level 1-case 7 (Table 4); (b) Level 2-case 7 (Table 5); (c) Level 3.
© Copyright Policy
Related In: Results  -  Collection

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

f12-sensors-12-03528: Summative method (Σ) rose plots (a) Level 1-case 7 (Table 4); (b) Level 2-case 7 (Table 5); (c) Level 3.
Mentions: The last part of this section describes the graphical representation of the quality measures for the three levels of resolution. For the summative method, only the best results (cases 7–l1, 7–l2 and 3–l4) achieved for each level are shown in Figure 12. For this purpose rose plots [51] have been used. These diagrams are constructed from the information contained in confusions matrices, their different sectors indicate the percentages of true and false positives, as well as the corresponding negatives. The high proportions of true positives and negatives vs. the lower proportions of false negatives reveal the good performance of this informational fusion method for levels 1 and 2 for the given cases. This tendency changes for level 3, where the sectors corresponding to the false positives and negatives increase noticeably.

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 Politécnica 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.