Limits...
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.


Related in: MedlinePlus

Level 1. Change Detection documents (λCVA = 0.6, λNDVI = 0.4) (a) Summative method; (b) Multiplicative method.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3376593&req=5

f9-sensors-12-03528: Level 1. Change Detection documents (λCVA = 0.6, λNDVI = 0.4) (a) Summative method; (b) Multiplicative method.

Mentions: The results reached for both methods, summative and multiplicative, are shown graphically in Figure 9. It is interesting to observe how the multiplicative method helps to reduce, to a certain extent, some degree of noise, while preserving the most representative changed or unchanged areas as detected by the corresponding change detection indices. However, this may lead to no desired consequence, when a specific index underestimates seriously one of the two categories. This problem will be further appreciated in level 3. Nonetheless, for this resolution level and dataset, these two informational fusion procedures appear to be suitable methods for categorizing change and no-change classes. Moreover, the summative process allows also identifying the best CD index or combination of indices.


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. Change Detection documents (λCVA = 0.6, λNDVI = 0.4) (a) Summative method; (b) Multiplicative method.
© Copyright Policy
Related In: Results  -  Collection

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

f9-sensors-12-03528: Level 1. Change Detection documents (λCVA = 0.6, λNDVI = 0.4) (a) Summative method; (b) Multiplicative method.
Mentions: The results reached for both methods, summative and multiplicative, are shown graphically in Figure 9. It is interesting to observe how the multiplicative method helps to reduce, to a certain extent, some degree of noise, while preserving the most representative changed or unchanged areas as detected by the corresponding change detection indices. However, this may lead to no desired consequence, when a specific index underestimates seriously one of the two categories. This problem will be further appreciated in level 3. Nonetheless, for this resolution level and dataset, these two informational fusion procedures appear to be suitable methods for categorizing change and no-change classes. Moreover, the summative process allows also identifying the best CD index or combination of indices.

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.


Related in: MedlinePlus