Multi-scale Spectral-Spatial Remote Sensing Classification of Coral Reef Habitats Using CNN-SVM

Wan, Jiaxin; Ma, Yi

doi:10.2112/si102-002.1

Cited by 11 publications

(8 citation statements)

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“…IKONOS-2 belongs to this category and can be found in several studies of benthic habitat mapping [86][87][88][89], but mostly before 2015, the year it has ceased operating. GaoFen-2 satellite, launched in 2014, has the same spatial and spectral resolution as IKONOS-2, but is not as widely used [90], perhaps because of its age: it was launched in 2014, when some sensors already had a better resolution. GaoFen have different satellites (from GaoFen-1 to GaoFen-14) that have the same or a lower resolution than GaoFen-2.…”

Section: Satellite Datamentioning

confidence: 99%

“…The most common and most precise satellite images come from WorldView satellites. For instance, WorldView-2 (WV-2), launched in 2009, has been widely used for benthic habitat mapping and coastline extraction [40,76,90,[100][101][102][103][104][105][106][107]. Despite the high-resolution images provided by WV-2, the highest quality images available at the current time come from WorldView-3 (WV-3), launched in 2014 [39,[108][109][110].…”

Section: Satellite Datamentioning

confidence: 99%

“…NN are commonly used to classify coral species with underwater images, but to date have rarely been used to map coral reefs from satellite images alone [90,206,207]. However, NN often appear in papers where satellite images are mixed with other sources such as aerial photographs, bathymetry data, or underwater images [208,209].…”

Section: Neural Networkmentioning

confidence: 99%

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Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

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Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

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Section: Satellite Datamentioning

confidence: 99%

Section: Satellite Datamentioning

confidence: 99%

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Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

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“…The goal of these studies is to identify the best performing classifier by comparing the results of the classifiers on the same datasets, usually using the same or similar training and validation data. The results of these studies have, thus far, been inconclusive, in regard to which classifier (DL or ML) performs best for many environments, though the current available literature can provide guidance for professionals looking to use such tools for particular applications (e.g., land use/land cover classification, vegetation cover, and coral reef habitat classification) [25][26][27]. Certainly, each tool should be selected based on how it best answers the research question.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

Morgan

Wang

et al. 2022

IJGI

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Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments, in comparison with the popularly of applied machine learning classifiers. This study seeks to explore the feasibility of using a U-Net deep learning architecture to classify bi-temporal, high-resolution, county-scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019, covering Beaufort County, South Carolina. The U-Net CNN classification results were compared with two machine learning classifiers, the random trees (RT) and support vector machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%), as opposed to the SVM (81.6%) and RT (75.7%) classifiers, for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible and powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR and multispectral data) to enhance classification accuracy.

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“…The goal of these studies is to identify the best performing classifier by comparing the results of the classifiers on the same datasets, usually using the same or similar training and validation data. The results of these studies have thus far been inconclusive as to which classifier (DL or ML) performs best for many environments, though the current available literature can provide guidance for professionals looking to use such tools for particular applications (e.g., land use/land cover classification, vegetation cover, coral reef habitat classification) [25][26][27]. Certainly, each tool should be selected based on how it best answers the research question.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

Morgan¹,

Wang²,

Li³

et al. 2021

Preprint

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Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments in comparison with the popularly applied machine learning classifiers. This study seeks to explore the feasibility for using a U-Net deep learning architecture to classify bi-temporal high resolution county scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019 covering Beaufort County, South Carolina. The U-net CNN classification results were compared with two machine learning classifiers, the Random Trees (RT) and the Support Vector Machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%) as opposed to the SVM (81.6%) and RT (75.7%) classifiers for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible nad powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR, multispectral data) to enhance classification accuracy.

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Multi-scale Spectral-Spatial Remote Sensing Classification of Coral Reef Habitats Using CNN-SVM

Cited by 11 publications

References 0 publications

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

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