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Volume 114 (2022): Issue 1 (December 2022)

Volume 113 (2022): Issue 1 (June 2022)

Volume 112 (2021): Issue 1 (December 2021)

Volume 111 (2021): Issue 1 (June 2021)

Volume 110 (2020): Issue 1 (December 2020)

Volume 109 (2020): Issue 1 (June 2020)

Volume 108 (2019): Issue 1 (December 2019)

Volume 107 (2019): Issue 1 (June 2019)

Volume 106 (2018): Issue 1 (December 2018)

Volume 105 (2018): Issue 1 (June 2018)

Volume 104 (2017): Issue 1 (December 2017)

Volume 103 (2017): Issue 1 (June 2017)

Volume 102 (2016): Issue 1 (December 2016)

Volume 101 (2016): Issue 1 (June 2016)

Volume 100 (2016): Issue 1 (May 2016)

Volume 99 (2015): Issue 1 (December 2015)

Volume 98 (2015): Issue 1 (July 2015)

Volume 97 (2014): Issue 1 (December 2014)

Volume 96 (2014): Issue 1 (June 2014)

Volume 95 (2013): Issue 1 (December 2013)

Volume 94 (2013): Issue 1 (October 2013)

Journal Details
Format
Journal
eISSN
2391-8152
First Published
20 Oct 2013
Publication timeframe
1 time per year
Languages
English

Search

Volume 114 (2022): Issue 1 (December 2022)

Journal Details
Format
Journal
eISSN
2391-8152
First Published
20 Oct 2013
Publication timeframe
1 time per year
Languages
English

Search

2 Articles
Open Access

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

Published Online: 28 Oct 2022
Page range: 1 - 7

Abstract

Abstract

This study compares two interpolation methods in the problem of a local GNSS/levelling (quasi) geoid modelling. It uses raw data, no global geopotential model is involved. The methods differ as to the complexity of modelling procedure and theoretical background, they are ordinary kriging/least-squares collocation with constant trend and inverse distance weighting (IDW). The comparison itself was done through leave-one-out and random (Monte Carlo) cross-validation. Ordinary kriging and IDW performance was tested with a local (using limited number of data) and global (using all available data) neighbourhoods using various planar covariance function models in case of kriging and various exponents (power parameter) in case of IDW. For the study area both methods assure an overall accuracy level, measured by mean absolute error, root mean square error and median absolute error, of less than 1 cm. Although the method of IDW is much simpler, a suitably selected parameters (also trend removal) may contribute to differences between methods that are virtually negligible (fraction of a millimetre).

Keywords

  • quasigeoid
  • kriging
  • least squares collocation
  • IDW
  • conversion surface
  • cross-validation
Open Access

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

Published Online: 28 Oct 2022
Page range: 9 - 14

Abstract

Abstract

Accurate and efficient detection of ships from aerial images is an intriguing and difficult task of extreme societal importance due to their implication and association with maritime infractions, and other suspicious actions. Having an automated system with the required capabilities indicates a substantial reduction in the related man-hours of characterization and the overall underlying processes. With the advent of various image processing techniques and advancements in the field of machine learning and deep learning, specialized methodologies can be created for the said task. An intuition for the enhancement of existing methodologies would be a study on attention-based cognition and the development of improved neural architectures with the available attention modules. This paper offers a novel study and empirical analysis of the utility of various attention modules with U-Net and other subsidiary architectures as a backbone for the task of computationally efficient and accurate ship detection. The best performing models are depicted and explained thoroughly, while considering their temporal performance.

Keywords

  • deep learning
  • attention
  • ship detection
  • U-Net
2 Articles
Open Access

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

Published Online: 28 Oct 2022
Page range: 1 - 7

Abstract

Abstract

This study compares two interpolation methods in the problem of a local GNSS/levelling (quasi) geoid modelling. It uses raw data, no global geopotential model is involved. The methods differ as to the complexity of modelling procedure and theoretical background, they are ordinary kriging/least-squares collocation with constant trend and inverse distance weighting (IDW). The comparison itself was done through leave-one-out and random (Monte Carlo) cross-validation. Ordinary kriging and IDW performance was tested with a local (using limited number of data) and global (using all available data) neighbourhoods using various planar covariance function models in case of kriging and various exponents (power parameter) in case of IDW. For the study area both methods assure an overall accuracy level, measured by mean absolute error, root mean square error and median absolute error, of less than 1 cm. Although the method of IDW is much simpler, a suitably selected parameters (also trend removal) may contribute to differences between methods that are virtually negligible (fraction of a millimetre).

Keywords

  • quasigeoid
  • kriging
  • least squares collocation
  • IDW
  • conversion surface
  • cross-validation
Open Access

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

Published Online: 28 Oct 2022
Page range: 9 - 14

Abstract

Abstract

Accurate and efficient detection of ships from aerial images is an intriguing and difficult task of extreme societal importance due to their implication and association with maritime infractions, and other suspicious actions. Having an automated system with the required capabilities indicates a substantial reduction in the related man-hours of characterization and the overall underlying processes. With the advent of various image processing techniques and advancements in the field of machine learning and deep learning, specialized methodologies can be created for the said task. An intuition for the enhancement of existing methodologies would be a study on attention-based cognition and the development of improved neural architectures with the available attention modules. This paper offers a novel study and empirical analysis of the utility of various attention modules with U-Net and other subsidiary architectures as a backbone for the task of computationally efficient and accurate ship detection. The best performing models are depicted and explained thoroughly, while considering their temporal performance.

Keywords

  • deep learning
  • attention
  • ship detection
  • U-Net

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