Eintrag weiter verarbeiten
WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE
Gespeichert in:
| 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, 2018, S. 1789-1794 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| Schlagwörter: |
| author_facet |
Wang, X. Xu, L. Wang, X. Xu, L. |
|---|---|
| author |
Wang, X. Xu, L. |
| spellingShingle |
Wang, X. Xu, L. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE General Earth and Planetary Sciences General Environmental Science |
| author_sort |
wang, x. |
| spelling |
Wang, X. Xu, L. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-3-1789-2018 <jats:p>Abstract. One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method’s performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments. </jats:p> WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| doi_str_mv |
10.5194/isprs-archives-xlii-3-1789-2018 |
| facet_avail |
Online Free |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNTE5NC9pc3Bycy1hcmNoaXZlcy14bGlpLTMtMTc4OS0yMDE4 |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNTE5NC9pc3Bycy1hcmNoaXZlcy14bGlpLTMtMTc4OS0yMDE4 |
| institution |
DE-D275 DE-Bn3 DE-Brt1 DE-D161 DE-Zwi2 DE-Gla1 DE-Zi4 DE-15 DE-Pl11 DE-Rs1 DE-105 DE-14 DE-Ch1 DE-L229 |
| imprint |
Copernicus GmbH, 2018 |
| imprint_str_mv |
Copernicus GmbH, 2018 |
| issn |
2194-9034 |
| issn_str_mv |
2194-9034 |
| language |
English |
| mega_collection |
Copernicus GmbH (CrossRef) |
| match_str |
wang2018waterbodiesextractionfromlandsat8oliimageryusingawaterindexsguiedstochasticfullyconnectedconditionalrandomfieldmodelandthesupportvectormachine |
| publishDateSort |
2018 |
| publisher |
Copernicus GmbH |
| recordtype |
ai |
| record_format |
ai |
| series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| source_id |
49 |
| title |
WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_unstemmed |
WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_full |
WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_fullStr |
WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_full_unstemmed |
WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_short |
WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_sort |
waterbodies extraction from landsat8-oli imagery using awater indexs-guied stochastic fully-connected conditional random field model and the support vector machine |
| topic |
General Earth and Planetary Sciences General Environmental Science |
| url |
http://dx.doi.org/10.5194/isprs-archives-xlii-3-1789-2018 |
| publishDate |
2018 |
| physical |
1789-1794 |
| description |
<jats:p>Abstract. One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method’s performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments.
</jats:p> |
| container_start_page |
1789 |
| container_title |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| container_volume |
XLII-3 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792330326153887748 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T13:23:14.319Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=WATERBODIES+EXTRACTION+FROM+LANDSAT8-OLI+IMAGERY+USING+AWATER+INDEXS-GUIED+STOCHASTIC+FULLY-CONNECTED+CONDITIONAL+RANDOM+FIELD+MODEL+AND+THE+SUPPORT+VECTOR+MACHINE&rft.date=2018-04-30&genre=article&issn=2194-9034&volume=XLII-3&spage=1789&epage=1794&pages=1789-1794&jtitle=The+International+Archives+of+the+Photogrammetry%2C+Remote+Sensing+and+Spatial+Information+Sciences&atitle=WATERBODIES+EXTRACTION+FROM+LANDSAT8-OLI+IMAGERY+USING+AWATER+INDEXS-GUIED+STOCHASTIC+FULLY-CONNECTED+CONDITIONAL+RANDOM+FIELD+MODEL+AND+THE+SUPPORT+VECTOR+MACHINE&aulast=Xu&aufirst=L.&rft_id=info%3Adoi%2F10.5194%2Fisprs-archives-xlii-3-1789-2018&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792330326153887748 |
| author | Wang, X., Xu, L. |
| author_facet | Wang, X., Xu, L., Wang, X., Xu, L. |
| author_sort | wang, x. |
| container_start_page | 1789 |
| container_title | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| container_volume | XLII-3 |
| description | <jats:p>Abstract. One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method’s performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments. </jats:p> |
| doi_str_mv | 10.5194/isprs-archives-xlii-3-1789-2018 |
| facet_avail | Online, Free |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuNTE5NC9pc3Bycy1hcmNoaXZlcy14bGlpLTMtMTc4OS0yMDE4 |
| imprint | Copernicus GmbH, 2018 |
| imprint_str_mv | Copernicus GmbH, 2018 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-D161, DE-Zwi2, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
| issn | 2194-9034 |
| issn_str_mv | 2194-9034 |
| language | English |
| last_indexed | 2024-03-01T13:23:14.319Z |
| match_str | wang2018waterbodiesextractionfromlandsat8oliimageryusingawaterindexsguiedstochasticfullyconnectedconditionalrandomfieldmodelandthesupportvectormachine |
| mega_collection | Copernicus GmbH (CrossRef) |
| physical | 1789-1794 |
| publishDate | 2018 |
| publishDateSort | 2018 |
| publisher | Copernicus GmbH |
| record_format | ai |
| recordtype | ai |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| source_id | 49 |
| spelling | Wang, X. Xu, L. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xlii-3-1789-2018 <jats:p>Abstract. One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method’s performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments. </jats:p> WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Wang, X., Xu, L., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE, General Earth and Planetary Sciences, General Environmental Science |
| title | WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_full | WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_fullStr | WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_full_unstemmed | WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_short | WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| title_sort | waterbodies extraction from landsat8-oli imagery using awater indexs-guied stochastic fully-connected conditional random field model and the support vector machine |
| title_unstemmed | WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE |
| topic | General Earth and Planetary Sciences, General Environmental Science |
| url | http://dx.doi.org/10.5194/isprs-archives-xlii-3-1789-2018 |