Proceedings ArticleDOI
Detection of Coastal Green Macroalgae based on SLIC, CNN and SVM
Jinghu Li,Lili Wang,Qianguo Xing +2 more
- pp 1-5
Reads0
Chats0
TLDR
Wang et al. as mentioned in this paper proposed a coastal green macroalgae extraction method based on the SLIC superpixel segmentation, CNN and SVM to realize the automated recognition of green microalgae from lots of high-resolution RGB video data collected by unmanned aerial vehicle (UAV) and handheld devices.Abstract:
Video surveillance is an important method to obtain the dynamic changes of green macroalgae along the coast. The paper proposes a coastal green macroalgae extraction method based on the SLIC superpixel segmentation, CNN and SVM to realize the automated recognition of green macroalgae from lots of high-resolution RGB video data collected by unmanned aerial vehicle (UAV) and handheld devices. Firstly, SLIC algorithm is used to generate the multi-scale patches on the original high-resolution image. Then, three classification CNN is used to divide the multi-scale patches into three types: green macroalgae, background and mixing. Finally, SVM algorithm is used to extract the green macroalgae to improve the accuracy at the pixel level in the mixed patches. In order to evaluate the performance of the proposed method, experiments are conducted on our coastal green macroalgae image dataset. Compared with the method of RGB vegetation indices (such as ExR, RGBVI, NGBDI), the overall accuracy (OA), F1 score, and Kappa of the green macroalgae extraction with the method proposed in this paper are up to 95.23%, 0.9612, 0.9436, respectively. The results show that our method is significantly better than that of RGB vegetation indices since it effectively reduces the influence of sea waves and light on the recognition results. The automated extraction method for coastal green macroalgae proposed in this paper can provide a reference for the automatic monitoring of coastal green macroalgae with high precision.read more
References
More filters
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Macroalgal blooms in shallow estuaries: Controls and ecophysiological and ecosystem consequences
Ivan Valiela,James W. McClelland,Jennifer Hauxwell,Peter J. Behr,Douglas Hersh,Kenneth H. Foreman +5 more
TL;DR: In this paper, the authors review features of macroalgal blooms pointed out in recent literature and summarize work done in the Waquoit Bay Land Margin Ecosystems Research project which suggests that nutrient loads, water residence times, presence of fringing salt marshes, and grazing affect macroalgae blooms.
Journal ArticleDOI
A novel ocean color index to detect floating algae in the global oceans
TL;DR: In this article, a simple ocean color index, namely the Floating Algae Index (FAI), is developed and used to detect floating algae in open ocean environments using the medium-resolution (250- and 500-m) data from operational MODIS (Moderate Resolution Imaging Spectroradiometer) instruments.
Journal ArticleDOI
‘Green tides’ are overwhelming the coastline of our blue planet: taking the world’s largest example
Naihao Ye,Xiaowen Zhang,Yuze Mao,Chengwei Liang,Dong Xu,Jian Zou,Zhimeng Zhuang,Qingyin Wang +7 more
TL;DR: A broad spectrum of events that come under the category of green tide are recognized world-wide as a response to elevated levels of seawater nutrients in coastal areas as discussed by the authors, and they involve a wide diversity of sites, macroalgal species, consequences, and possible causes.
Journal ArticleDOI
Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations
Mengqiu Wang,Chuanmin Hu +1 more
TL;DR: In this paper, a novel approach is developed to detect Sargassum presence and to quantify Sargasso coverage using the Moderate Resolution Imaging Spectroradiometer (MODIS) alternative floating algae index (AFAI), which examines the red-edge reflectance of floating vegetation.
Related Papers (5)
Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation
One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California
Daniel Guidici,Matthew L. Clark +1 more