A Closer Look at Seagrass Meadows: Semantic Segmentation for Visual Coverage Estimation

Weidmann, Franz; Jäger, Jonas; Reus, Gereon; Schultz, Stewart T.; Kruschel, Claudia; Wolff, Viviane; Fricke-Neuderth, Klaus

doi:10.1109/oceanse.2019.8867064

Cited by 20 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, seagrass mapping can also be considered as a regression problem instead of classification [16,78]. Other work using FCNNs for seagrass mapping was found in [79][80][81]. However, these studies were mainly concerned with subtidal seagrass meadows instead of intertidal seagrass.…”

Section: Overall Analysismentioning

confidence: 99%

Semi-supervised segmentation for coastal monitoring seagrass using RPA imagery

Hobley¹,

Arosio²,

French³

et al. 2021

Preprint

View full text Add to dashboard Cite

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improve the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared: Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps, however FCNNs are an emerging set of algorithms within Deep Learning with sparse application towards seagrass mapping. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiscale segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with standard OBIA method used by ecologists, while also noting an increase in performance for mapping ecological features that are sparsely labelled across the study site.

show abstract

Section: Overall Analysismentioning

confidence: 99%

Semi-supervised segmentation for coastal monitoring seagrass using RPA imagery

Hobley¹,

Arosio²,

French³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The number of correct classifications of each kind of phenomenon detected by DeepLabV3+ and the MFNN is counted, and a histogram is plotted in Figure 8 to compare the results. In the research of some existing remote sensing image detection usages, DeepLabV3+ has shown excellent performance and better detection accuracy compared to other networks such as PspNet, SegNet, U-net, FCN, and so on [42][43][44][45][46][47][48]. To demonstrate the superior performance of MFNN, we conducted experiments using MFNN and DeepLabV3+, respectively.…”

Section: Experiments On the Single Type Of Oceanic Phenomena In Sar Imagesmentioning

confidence: 99%

Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images

Yan

Chong

Zhao

et al. 2019

Sensors

View full text Add to dashboard Cite

Oceanic phenomena detection in synthetic aperture radar (SAR) images is important in the fields of fishery, military, and oceanography. The traditional detection methods of oceanic phenomena in SAR images are based on handcrafted features and detection thresholds, which have a problem of poor generalization ability. Methods based on deep learning have good generalization ability. However, most of the deep learning methods currently applied to oceanic phenomena detection only detect one type of phenomenon. To satisfy the requirements of efficient and accurate detection of multiple information of multiple oceanic phenomena in massive SAR images, this paper proposes an oceanic phenomena detection method in SAR images based on convolutional neural network (CNN). The method first uses ResNet-50 to extract multilevel features. Second, it uses the atrous spatial pyramid pooling (ASPP) module to extract multiscale features. Finally, it fuses multilevel features and multiscale features to detect oceanic phenomena. The SAR images acquired from the Sentinel-1 satellite are used to establish a sample dataset of oceanic phenomena. The method proposed can achieve 91% accuracy on the dataset.

show abstract

“…Deep neural networks use automated parameter optimization to extract the features of objects using colors, textures, and shapes of possibly segmented, meaningful regions in the image, thus enabling classification with high versatility [48]. Deep neural networks are also used in some cases of seagrass mapping [49,50]. Perez [49] applied deep neural networks to quantify seagrass distribution based on multiband satellite images (ground sample distance of 1.24 m) and showed that deep neural networks achieved much better results than a linear regression model and a support vector machine did.…”

Section: Introductionmentioning

confidence: 99%

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

et al. 2021

View full text Add to dashboard Cite

Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs’ feeding trails, which are unvegetated winding tracks left after feeding, have been used as an indicator of their feeding ground utilization. However, current ground-based measurements of these trails require a large amount of time and effort. Here, we developed effective methods to observe the dugongs’ feeding trails using unmanned aerial vehicle (UAV) images (1) by extracting the dugong feeding trails using deep neural networks. Furthermore, we demonstrated two applications as follows; (2) extraction of the daily new feeding trails with deep neural networks and (3) estimation the direction of the feeding trails. We obtained aerial photographs from the intertidal seagrass bed at Talibong Island, Trang Province, Thailand. The F1 scores, which are a measure of binary classification model’s accuracy taking false positives and false negatives into account, for the method (1) were 89.5% and 87.7% for the images with ground sampling resolutions of 1 cm/pixel and 0.5 cm/pixel, respectively, while the F1 score for the method (2) was 61.9%. The F1 score for the method (1) was high enough to perform scientific studies on the dugong. However, the method (2) should be improved, and there remains a need for manual correction. The mean area of the extracted daily new feeding trails from September 12–27, 2019, was 187.8 m2 per day (n = 9). Total 63.9% of the feeding trails was estimated to have direction within a range of 112.5° and 157.5°. These proposed new methods will reduce the time and efforts required for future feeding trail observations and contribute to future assessments of the dugongs’ seagrass habitat use.

show abstract

A Closer Look at Seagrass Meadows: Semantic Segmentation for Visual Coverage Estimation

Cited by 20 publications

References 13 publications

Semi-supervised segmentation for coastal monitoring seagrass using RPA imagery

Semi-supervised segmentation for coastal monitoring seagrass using RPA imagery

Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

Contact Info

Product

Resources

About