Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis

Y. Liu; Y-D. Zhang; Z. Huang; M-M. Wang; D. Yang; H-M. Ma; Y-X. Zhang; Y-F. Li; H-W. Li; X-G. Hu

doi:10.3990/2.452

Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis

Y. Liu^*, Y-D. Zhang, Z. Huang, M-M. Wang, D. Yang, H-M. Ma, Y-X. Zhang, Y-F. Li, H-W. Li, X-G. Hu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

To create image objects for subsequent classification in object-based image analysis, an optimal segmentation threshold (OST) is a pre-requisite for image segmentation. However, an OST is practically acquired by assessing and ranking an exhaustive segment data stack constructed after foregoing image segmentation. In this paper, we propose an iterative exploration method via recognition of the Euclidean distance 2 (ED2)–scale parameter (SP) pattern with the least five tiles of segment data stack in each cycle among the
Potential Segmentation Error (PSE)–Number-of-Segments Ratio (NSR)–ED2 patterns along with SP. We conducted two experiments. In the first experiment, we validated the general italic U-shaped ED2–SP pattern by constructing exhaustive segment data stacks and corresponding segment data stacks. In the second experiment, we adopted the proposed iterative exploration method for OST selection based on the ED2–SP pattern with respect to five equal-spacing SPs in each cycle. The bottom of the pattern was persistently approached by constructing updated segment data stacks and corresponding segment data stacks with five dynamically adjusted tiles. Our results showed that the PSE–NSR–ED2 discrepancy measure system is advantageous to OST selection.

Original language	English
Title of host publication	Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands
Editors	N. Kerle, M. Gerke, S. Lefevre
Place of Publication	Enschede
Publisher	University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
Number of pages	8
ISBN (Print)	978-90-365-4201-2
DOIs	https://doi.org/10.3990/2.452
Publication status	Published - 14 Sept 2016
Externally published	Yes
Event	6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016: Solutions & Synergies - University of Twente Faculty of Geo-Information and Earth Observation (ITC), Enschede, Netherlands Duration: 14 Sept 2016 → 16 Sept 2016 Conference number: 6 https://www.geobia2016.com/

Conference

Conference	6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016
Abbreviated title	GEOBIA
Country/Territory	Netherlands
City	Enschede
Period	14/09/16 → 16/09/16
Internet address	https://www.geobia2016.com/

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10.3990/2.452

Liu-Segmentation Optimization In Object-Based Image Analysis Through Recognizing Patterns Between PSE-NSR-ED2Final published version, 366 KB

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GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book
Kerle, N. (Editor), Gerke, M. (Editor) & Lefèvre, S. (Editor), 2016, Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC).
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Liu, Y., Zhang, Y.-D., Huang, Z., Wang, M.-M., Yang, D., Ma, H.-M., Zhang, Y.-X., Li, Y.-F., Li, H.-W., & Hu, X.-G. (2016). Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis. In N. Kerle, M. Gerke, & S. Lefevre (Eds.), Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/2.452

Liu, Y. ; Zhang, Y-D. ; Huang, Z. et al. / Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis. Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. editor / N. Kerle ; M. Gerke ; S. Lefevre. Enschede : University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2016.

@inproceedings{7570afb0e7d347fea6b69cf215bdee60,

title = "Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis",

abstract = "To create image objects for subsequent classification in object-based image analysis, an optimal segmentation threshold (OST) is a pre-requisite for image segmentation. However, an OST is practically acquired by assessing and ranking an exhaustive segment data stack constructed after foregoing image segmentation. In this paper, we propose an iterative exploration method via recognition of the Euclidean distance 2 (ED2)–scale parameter (SP) pattern with the least five tiles of segment data stack in each cycle among thePotential Segmentation Error (PSE)–Number-of-Segments Ratio (NSR)–ED2 patterns along with SP. We conducted two experiments. In the first experiment, we validated the general italic U-shaped ED2–SP pattern by constructing exhaustive segment data stacks and corresponding segment data stacks. In the second experiment, we adopted the proposed iterative exploration method for OST selection based on the ED2–SP pattern with respect to five equal-spacing SPs in each cycle. The bottom of the pattern was persistently approached by constructing updated segment data stacks and corresponding segment data stacks with five dynamically adjusted tiles. Our results showed that the PSE–NSR–ED2 discrepancy measure system is advantageous to OST selection.",

author = "Y. Liu and Y-D. Zhang and Z. Huang and M-M. Wang and D. Yang and H-M. Ma and Y-X. Zhang and Y-F. Li and H-W. Li and X-G. Hu",

year = "2016",

month = sep,

day = "14",

doi = "10.3990/2.452",

language = "English",

isbn = "978-90-365-4201-2",

editor = "N. Kerle and M. Gerke and S. Lefevre",

booktitle = "Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands",

publisher = "University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)",

note = "6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016 : Solutions & Synergies, GEOBIA ; Conference date: 14-09-2016 Through 16-09-2016",

url = "https://www.geobia2016.com/",

}

Liu, Y, Zhang, Y-D, Huang, Z, Wang, M-M, Yang, D, Ma, H-M, Zhang, Y-X, Li, Y-F, Li, H-W & Hu, X-G 2016, Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis. in N Kerle, M Gerke & S Lefevre (eds), Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, 6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016, Enschede, Netherlands, 14/09/16. https://doi.org/10.3990/2.452

Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis. / Liu, Y.; Zhang, Y-D.; Huang, Z. et al.
Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. ed. / N. Kerle; M. Gerke; S. Lefevre. Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2016.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis

AU - Liu, Y.

AU - Zhang, Y-D.

AU - Huang, Z.

AU - Wang, M-M.

AU - Yang, D.

AU - Ma, H-M.

AU - Zhang, Y-X.

AU - Li, Y-F.

AU - Li, H-W.

AU - Hu, X-G.

N1 - Conference code: 6

PY - 2016/9/14

Y1 - 2016/9/14

N2 - To create image objects for subsequent classification in object-based image analysis, an optimal segmentation threshold (OST) is a pre-requisite for image segmentation. However, an OST is practically acquired by assessing and ranking an exhaustive segment data stack constructed after foregoing image segmentation. In this paper, we propose an iterative exploration method via recognition of the Euclidean distance 2 (ED2)–scale parameter (SP) pattern with the least five tiles of segment data stack in each cycle among thePotential Segmentation Error (PSE)–Number-of-Segments Ratio (NSR)–ED2 patterns along with SP. We conducted two experiments. In the first experiment, we validated the general italic U-shaped ED2–SP pattern by constructing exhaustive segment data stacks and corresponding segment data stacks. In the second experiment, we adopted the proposed iterative exploration method for OST selection based on the ED2–SP pattern with respect to five equal-spacing SPs in each cycle. The bottom of the pattern was persistently approached by constructing updated segment data stacks and corresponding segment data stacks with five dynamically adjusted tiles. Our results showed that the PSE–NSR–ED2 discrepancy measure system is advantageous to OST selection.

AB - To create image objects for subsequent classification in object-based image analysis, an optimal segmentation threshold (OST) is a pre-requisite for image segmentation. However, an OST is practically acquired by assessing and ranking an exhaustive segment data stack constructed after foregoing image segmentation. In this paper, we propose an iterative exploration method via recognition of the Euclidean distance 2 (ED2)–scale parameter (SP) pattern with the least five tiles of segment data stack in each cycle among thePotential Segmentation Error (PSE)–Number-of-Segments Ratio (NSR)–ED2 patterns along with SP. We conducted two experiments. In the first experiment, we validated the general italic U-shaped ED2–SP pattern by constructing exhaustive segment data stacks and corresponding segment data stacks. In the second experiment, we adopted the proposed iterative exploration method for OST selection based on the ED2–SP pattern with respect to five equal-spacing SPs in each cycle. The bottom of the pattern was persistently approached by constructing updated segment data stacks and corresponding segment data stacks with five dynamically adjusted tiles. Our results showed that the PSE–NSR–ED2 discrepancy measure system is advantageous to OST selection.

U2 - 10.3990/2.452

DO - 10.3990/2.452

M3 - Conference contribution

SN - 978-90-365-4201-2

BT - Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands

A2 - Kerle, N.

A2 - Gerke, M.

A2 - Lefevre, S.

PB - University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)

CY - Enschede

T2 - 6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016

Y2 - 14 September 2016 through 16 September 2016

ER -

Liu Y, Zhang YD, Huang Z, Wang MM, Yang D, Ma HM et al. Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis. In Kerle N, Gerke M, Lefevre S, editors, Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). 2016 doi: 10.3990/2.452

Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis

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GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

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