A novel mathematical morphology based algorithm for shoreline extraction from satellite images
Shoreline extraction is fundamental and inevitable for several studies. Ascertaining the precise spatial location of the shoreline is crucial. Recently, the need for using remote sensing data to accomplish the complex task of automatic extraction of features, such as shoreline, has considerably incr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-10-01
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| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10095020.2017.1403089 |
| _version_ | 1818340115643105280 |
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| author | C. A. Rishikeshan H. Ramesh |
| author_facet | C. A. Rishikeshan H. Ramesh |
| author_sort | C. A. Rishikeshan |
| collection | DOAJ |
| description | Shoreline extraction is fundamental and inevitable for several studies. Ascertaining the precise spatial location of the shoreline is crucial. Recently, the need for using remote sensing data to accomplish the complex task of automatic extraction of features, such as shoreline, has considerably increased. Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating. Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps. Here, we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries. The salient features of this work are the preservation of actual size and shape of the shorelines, run-time structuring element definition, semi-automation, faster processing, and single band adaptability. The proposed approach is tested with various sensor-driven images with low to high resolutions. Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach. The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments. |
| first_indexed | 2024-12-13T15:37:46Z |
| format | Article |
| id | doaj.art-976f658b2a374989be56a6075f3f365a |
| institution | Directory Open Access Journal |
| issn | 1009-5020 1993-5153 |
| language | English |
| last_indexed | 2024-12-13T15:37:46Z |
| publishDate | 2017-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj.art-976f658b2a374989be56a6075f3f365a2022-12-21T23:39:57ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532017-10-0120434535210.1080/10095020.2017.14030891403089A novel mathematical morphology based algorithm for shoreline extraction from satellite imagesC. A. Rishikeshan0H. Ramesh1National Institute of Technology, KarnatakaNational Institute of Technology, KarnatakaShoreline extraction is fundamental and inevitable for several studies. Ascertaining the precise spatial location of the shoreline is crucial. Recently, the need for using remote sensing data to accomplish the complex task of automatic extraction of features, such as shoreline, has considerably increased. Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating. Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps. Here, we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries. The salient features of this work are the preservation of actual size and shape of the shorelines, run-time structuring element definition, semi-automation, faster processing, and single band adaptability. The proposed approach is tested with various sensor-driven images with low to high resolutions. Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach. The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments.http://dx.doi.org/10.1080/10095020.2017.1403089Feature extractionshoreline detectionsatellite image processingremote sensingmathematical morphology |
| spellingShingle | C. A. Rishikeshan H. Ramesh A novel mathematical morphology based algorithm for shoreline extraction from satellite images Geo-spatial Information Science Feature extraction shoreline detection satellite image processing remote sensing mathematical morphology |
| title | A novel mathematical morphology based algorithm for shoreline extraction from satellite images |
| title_full | A novel mathematical morphology based algorithm for shoreline extraction from satellite images |
| title_fullStr | A novel mathematical morphology based algorithm for shoreline extraction from satellite images |
| title_full_unstemmed | A novel mathematical morphology based algorithm for shoreline extraction from satellite images |
| title_short | A novel mathematical morphology based algorithm for shoreline extraction from satellite images |
| title_sort | novel mathematical morphology based algorithm for shoreline extraction from satellite images |
| topic | Feature extraction shoreline detection satellite image processing remote sensing mathematical morphology |
| url | http://dx.doi.org/10.1080/10095020.2017.1403089 |
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