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AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS
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| Zeitschriftentitel: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W12-2020, 2020, S. 91-95 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| Schlagwörter: |
| author_facet |
Soares, A. R. Körting, T. S. Fonseca, L. M. G. Neves, A. K. Soares, A. R. Körting, T. S. Fonseca, L. M. G. Neves, A. K. |
|---|---|
| author |
Soares, A. R. Körting, T. S. Fonseca, L. M. G. Neves, A. K. |
| spellingShingle |
Soares, A. R. Körting, T. S. Fonseca, L. M. G. Neves, A. K. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS General Earth and Planetary Sciences General Environmental Science |
| author_sort |
soares, a. r. |
| spelling |
Soares, A. R. Körting, T. S. Fonseca, L. M. G. Neves, A. K. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-91-2020 <jats:p>Abstract. Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2 m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation. </jats:p> AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Copernicus GmbH, 2020 |
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Copernicus GmbH, 2020 |
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2020 |
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Copernicus GmbH |
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| title |
AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_unstemmed |
AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_full |
AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_fullStr |
AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_full_unstemmed |
AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_short |
AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_sort |
an unsupervised segmentation method for remote sensing imagery based on conditional random fields |
| topic |
General Earth and Planetary Sciences General Environmental Science |
| url |
http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-91-2020 |
| publishDate |
2020 |
| physical |
91-95 |
| description |
<jats:p>Abstract. Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2 m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation.
</jats:p> |
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| author | Soares, A. R., Körting, T. S., Fonseca, L. M. G., Neves, A. K. |
| author_facet | Soares, A. R., Körting, T. S., Fonseca, L. M. G., Neves, A. K., Soares, A. R., Körting, T. S., Fonseca, L. M. G., Neves, A. K. |
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| container_title | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| container_volume | XLII-3/W12-2020 |
| description | <jats:p>Abstract. Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2 m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation. </jats:p> |
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| imprint | Copernicus GmbH, 2020 |
| imprint_str_mv | Copernicus GmbH, 2020 |
| institution | DE-Pl11, DE-Rs1, DE-14, DE-105, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Zi4, DE-Gla1, DE-15 |
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| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| spelling | Soares, A. R. Körting, T. S. Fonseca, L. M. G. Neves, A. K. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-91-2020 <jats:p>Abstract. Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2 m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation. </jats:p> AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Soares, A. R., Körting, T. S., Fonseca, L. M. G., Neves, A. K., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS, General Earth and Planetary Sciences, General Environmental Science |
| title | AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_full | AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_fullStr | AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_full_unstemmed | AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_short | AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| title_sort | an unsupervised segmentation method for remote sensing imagery based on conditional random fields |
| title_unstemmed | AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS |
| topic | General Earth and Planetary Sciences, General Environmental Science |
| url | http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-91-2020 |