Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

Dang, Kinh Bac; Nguyen, Manh Ha; Nguyễn, Đức Anh; Phan, Thi Thanh Hai; Giang, Tuan Linh; Hải, Phạm Hoàng; Nguyen, Thu Nhung; Tran, Thi Thuy Van; Bui, Dieu Tien

doi:10.3390/rs12193270

Cited by 55 publications

(34 citation statements)

References 66 publications

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“…Specifically, U-Net has shown to outperform SVM and RF classifications for wetland mapping using Sentinel-2 10 m imagery. [31] discovered that the SVM and RT classifiers only achieved an OA of 50.5% and 46.4%, respectively, while the U-Net classifier regularly reached at least 85%, depending on the optimizing function used. Our study suggests the higher resolution NAIP imagery includes enough spatial detail to improve the OA to the detected levels of accuracy (e.g., U-Net = 92.4%; SVM = 81.6; RT = 75.7%).…”

Section: Discussionmentioning

confidence: 99%

“…However, the classification of the image itself took 43 hours and 23 minutes for a total of 46 hours and 22 minutes. The length of classification is not a common finding, however, as U-net classifiers have been found to be faster than many others in remote sensing applications [30][31]58]. The authors suggest the extra length of time required to complete classification was due to the machine specifications, the size of the dataset, and the methods by which the tiles were classified and subsequently mosaicked together.…”

Section: Comparison Of Model Performance and Accuraciesmentioning

confidence: 99%

“…The architecture of the network can be divided into two halves-the first being an encoding or 'contracting" side and a decoding or "expansive" side-that give the architecture its "u" shape. The U-net algorithm has shown success in classifying coastal wetlands using remotely sensed imagery in previous studies [30][31]. SVM and RF classifiers are commonly used ML classifiers for remote sensing analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

Section: Comparison Of Model Performance and Accuraciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Morgan¹,

Wang²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The architecture of the network can be divided into two halves-an encoding or 'contracting" side and decoding or "expansive" side-that give the architecture its "u" shape. The U-Net algorithm has shown success in classifying coastal wetlands, using the remotely-sensed imagery in previous studies [30,31]. SVM and RF classifiers are commonly used ML classifiers for remote sensing analysis.…”

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

View full text Add to dashboard Cite

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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“…Due to the complex ecological conditions of wetlands and the spatial and temporal limitations of field investigations, remote sensing technology has become an important means of wetland mapping and monitoring. Despite the success of optical satellite data in applications such as wetland detection and water level monitoring [3][4][5], optical images are less useful in coastal areas due to cloud cover [6]. Synthetic Aperture Radar (SAR), which provides valuable geophysical parameters over intertidal zones in all-weather and daylight-independent conditions [7][8][9], has emerged as a promising tool for wetland monitoring.…”

Section: Introductionmentioning

confidence: 99%

Coastal Wetland Classification with GF-3 Polarimetric SAR Imagery by Using Object-Oriented Random Forest Algorithm

Zhang

Chen

et al. 2021

Sensors

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When the use of optical images is not practical due to cloud cover, Synthetic Aperture Radar (SAR) imagery is a preferred alternative for monitoring coastal wetlands because it is unaffected by weather conditions. Polarimetric SAR (PolSAR) enables the detection of different backscattering mechanisms and thus has potential applications in land cover classification. Gaofen-3 (GF-3) is the first Chinese civilian satellite with multi-polarized C-band SAR imaging capability. Coastal wetland classification with GF-3 polarimetric SAR imagery has attracted increased attention in recent years, but it remains challenging. The aim of this study was to classify land cover in coastal wetlands using an object-oriented random forest algorithm on the basis of GF-3 polarimetric SAR imagery. First, a set of 16 commonly used SAR features was extracted. Second, the importance of each SAR feature was calculated, and the optimal polarimetric features were selected for wetland classification by combining random forest (RF) with sequential backward selection (SBS). Finally, the proposed algorithm was utilized to classify different land cover types in the Yancheng Coastal Wetlands. The results show that the most important parameters for wetland classification in this study were Shannon entropy, Span and orientation randomness, combined with features derived from Yamaguchi decomposition, namely, volume scattering, double scattering, surface scattering and helix scattering. When the object-oriented RF classification approach was used with the optimal feature combination, different land cover types in the study area were classified, with an overall accuracy of up to 92%.

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Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

Cited by 55 publications

References 66 publications

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

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

Coastal Wetland Classification with GF-3 Polarimetric SAR Imagery by Using Object-Oriented Random Forest Algorithm

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