An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images
The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) metho...
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2016
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Online Access: | https://doi.org/10.3390/rs8050375 https://doaj.org/article/4d3e4aa7084a4a95b3954d8761d3a1d7 |
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ftdoajarticles:oai:doaj.org/article:4d3e4aa7084a4a95b3954d8761d3a1d7 2023-05-15T13:52:05+02:00 An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images Chandi Witharana Heather J. Lynch 2016-04-01T00:00:00Z https://doi.org/10.3390/rs8050375 https://doaj.org/article/4d3e4aa7084a4a95b3954d8761d3a1d7 EN eng MDPI AG http://www.mdpi.com/2072-4292/8/5/375 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8050375 https://doaj.org/article/4d3e4aa7084a4a95b3954d8761d3a1d7 Remote Sensing, Vol 8, Iss 5, p 375 (2016) Antarctica penguins guano GEOBIA VHSR imagery census Science Q article 2016 ftdoajarticles https://doi.org/10.3390/rs8050375 2022-12-31T15:17:12Z The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census. Article in Journal/Newspaper Antarc* Antarctic Antarctica Directory of Open Access Journals: DOAJ Articles Antarctic Guano ENVELOPE(141.604,141.604,-66.775,-66.775) Remote Sensing 8 5 375 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Antarctica penguins guano GEOBIA VHSR imagery census Science Q |
spellingShingle |
Antarctica penguins guano GEOBIA VHSR imagery census Science Q Chandi Witharana Heather J. Lynch An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images |
topic_facet |
Antarctica penguins guano GEOBIA VHSR imagery census Science Q |
description |
The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census. |
format |
Article in Journal/Newspaper |
author |
Chandi Witharana Heather J. Lynch |
author_facet |
Chandi Witharana Heather J. Lynch |
author_sort |
Chandi Witharana |
title |
An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images |
title_short |
An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images |
title_full |
An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images |
title_fullStr |
An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images |
title_full_unstemmed |
An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images |
title_sort |
object-based image analysis approach for detecting penguin guano in very high spatial resolution satellite images |
publisher |
MDPI AG |
publishDate |
2016 |
url |
https://doi.org/10.3390/rs8050375 https://doaj.org/article/4d3e4aa7084a4a95b3954d8761d3a1d7 |
long_lat |
ENVELOPE(141.604,141.604,-66.775,-66.775) |
geographic |
Antarctic Guano |
geographic_facet |
Antarctic Guano |
genre |
Antarc* Antarctic Antarctica |
genre_facet |
Antarc* Antarctic Antarctica |
op_source |
Remote Sensing, Vol 8, Iss 5, p 375 (2016) |
op_relation |
http://www.mdpi.com/2072-4292/8/5/375 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8050375 https://doaj.org/article/4d3e4aa7084a4a95b3954d8761d3a1d7 |
op_doi |
https://doi.org/10.3390/rs8050375 |
container_title |
Remote Sensing |
container_volume |
8 |
container_issue |
5 |
container_start_page |
375 |
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1766256317844946944 |