Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)
Multisource remote sensing data acquisition has been increased in the last years due to technological improvements and decreased acquisition cost of remotely sensed data and products. This study attempts to fuse different types of prospection data acquired from dissimilar remote sensors and explores...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/10/11/1762 |
| _version_ | 1818320103796637696 |
|---|---|
| author | Athos Agapiou Apostolos Sarris |
| author_facet | Athos Agapiou Apostolos Sarris |
| author_sort | Athos Agapiou |
| collection | DOAJ |
| description | Multisource remote sensing data acquisition has been increased in the last years due to technological improvements and decreased acquisition cost of remotely sensed data and products. This study attempts to fuse different types of prospection data acquired from dissimilar remote sensors and explores new ways of interpreting remote sensing data obtained from archaeological sites. Combination and fusion of complementary sensory data does not only increase the detection accuracy but it also increases the overall performance in respect to recall and precision. Moving beyond the discussion and concerns related to fusion and integration of multisource prospection data, this study argues their potential (re)use based on Bayesian Neural Network (BNN) fusion models. The archaeological site of Vésztő-Mágor Tell in the eastern part of Hungary was selected as a case study, since ground penetrating radar (GPR) and ground spectral signatures have been collected in the past. GPR 20 cm depth slices results were correlated with spectroradiometric datasets based on neural network models. The results showed that the BNN models provide a global correlation coefficient of up to 73%—between the GPR and the spectroradiometric data—for all depth slices. This could eventually lead to the potential re-use of archived geo-prospection datasets with optical earth observation datasets. A discussion regarding the potential limitations and challenges of this approach is also included in the paper. |
| first_indexed | 2024-12-13T10:19:41Z |
| format | Article |
| id | doaj.art-2908551eeba443f88382c555cab225ad |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:19:41Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2908551eeba443f88382c555cab225ad2022-12-21T23:51:12ZengMDPI AGRemote Sensing2072-42922018-11-011011176210.3390/rs10111762rs10111762Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)Athos Agapiou0Apostolos Sarris1Department of Civil Engineering and Geomatics, Eratosthenes Research Center, Cyprus University of Technology, Saripolou 2-8, Limassol 3036, CyprusLaboratory of Geophysical-Satellite Remote Sensing and Archaeo-Environment, Foundation for Research and Technology, Hellas (F.O.R.T.H.), 74100 Rethymno, GreeceMultisource remote sensing data acquisition has been increased in the last years due to technological improvements and decreased acquisition cost of remotely sensed data and products. This study attempts to fuse different types of prospection data acquired from dissimilar remote sensors and explores new ways of interpreting remote sensing data obtained from archaeological sites. Combination and fusion of complementary sensory data does not only increase the detection accuracy but it also increases the overall performance in respect to recall and precision. Moving beyond the discussion and concerns related to fusion and integration of multisource prospection data, this study argues their potential (re)use based on Bayesian Neural Network (BNN) fusion models. The archaeological site of Vésztő-Mágor Tell in the eastern part of Hungary was selected as a case study, since ground penetrating radar (GPR) and ground spectral signatures have been collected in the past. GPR 20 cm depth slices results were correlated with spectroradiometric datasets based on neural network models. The results showed that the BNN models provide a global correlation coefficient of up to 73%—between the GPR and the spectroradiometric data—for all depth slices. This could eventually lead to the potential re-use of archived geo-prospection datasets with optical earth observation datasets. A discussion regarding the potential limitations and challenges of this approach is also included in the paper.https://www.mdpi.com/2072-4292/10/11/1762remote sensing archaeologyfusionneural networksre-useGPRspectral signaturesHungary |
| spellingShingle | Athos Agapiou Apostolos Sarris Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN) Remote Sensing remote sensing archaeology fusion neural networks re-use GPR spectral signatures Hungary |
| title | Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN) |
| title_full | Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN) |
| title_fullStr | Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN) |
| title_full_unstemmed | Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN) |
| title_short | Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN) |
| title_sort | beyond gis layering challenging the re use and fusion of archaeological prospection data based on bayesian neural networks bnn |
| topic | remote sensing archaeology fusion neural networks re-use GPR spectral signatures Hungary |
| url | https://www.mdpi.com/2072-4292/10/11/1762 |
| work_keys_str_mv | AT athosagapiou beyondgislayeringchallengingthereuseandfusionofarchaeologicalprospectiondatabasedonbayesianneuralnetworksbnn AT apostolossarris beyondgislayeringchallengingthereuseandfusionofarchaeologicalprospectiondatabasedonbayesianneuralnetworksbnn |