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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.


Level 2 thresholded indices: best results (a) Area A1: Diff. Index–Li method (binary image); (b) Area A1: NDVI–Otsu method (binary image); (c) Area A2: Diff. Index–Li method (binary image); (d) Area A2: Ratio Index–Otsu method (binary image).
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f7-sensors-12-03528: Level 2 thresholded indices: best results (a) Area A1: Diff. Index–Li method (binary image); (b) Area A1: NDVI–Otsu method (binary image); (c) Area A2: Diff. Index–Li method (binary image); (d) Area A2: Ratio Index–Otsu method (binary image).

Mentions: Regarding the variability range of the ratio index values, it appears to be shorter (NVrat_min = 0, NVrat_max = 9) than the values interval of the difference CD index (NVdif_min = 0, NVdif_max = 68). As it has been stated by [14], in these cases, iterative and clustering algorithms, as the Iter and Otsu procedures, perform better than entropic methods, as it happens to a certain extent with the Li method and clearly with the remaining entropy based algorithms. In order to verify this issue, a visual analysis has been carried out, which has definitely confirmed the suitability of the Otsu and Ridley-Iter methods. The final results corresponding to the most appropriate thresholding methods are depicted in Figure 7. Comparing these methods with the change detection indices shown in Figure 4, it may be stated that the applied procedures lead to a reliable representation of the change and no_change areas.


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 thresholded indices: best results (a) Area A1: Diff. Index–Li method (binary image); (b) Area A1: NDVI–Otsu method (binary image); (c) Area A2: Diff. Index–Li method (binary image); (d) Area A2: Ratio Index–Otsu method (binary image).
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-12-03528: Level 2 thresholded indices: best results (a) Area A1: Diff. Index–Li method (binary image); (b) Area A1: NDVI–Otsu method (binary image); (c) Area A2: Diff. Index–Li method (binary image); (d) Area A2: Ratio Index–Otsu method (binary image).
Mentions: Regarding the variability range of the ratio index values, it appears to be shorter (NVrat_min = 0, NVrat_max = 9) than the values interval of the difference CD index (NVdif_min = 0, NVdif_max = 68). As it has been stated by [14], in these cases, iterative and clustering algorithms, as the Iter and Otsu procedures, perform better than entropic methods, as it happens to a certain extent with the Li method and clearly with the remaining entropy based algorithms. In order to verify this issue, a visual analysis has been carried out, which has definitely confirmed the suitability of the Otsu and Ridley-Iter methods. The final results corresponding to the most appropriate thresholding methods are depicted in Figure 7. Comparing these methods with the change detection indices shown in Figure 4, it may be stated that the applied procedures lead to a reliable representation of the change and no_change areas.

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.