Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images—Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

Liang, Chenbin; Cheng, Bo; Xiao, Baihua; He, Chenlinqiu; Xunan, Liu; Jia, Ning; Chen, Jinfen

doi:10.3390/rs13061083

Cited by 20 publications

(10 citation statements)

References 19 publications

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“…However, many current studies have detected the mariculture area without considering the effect of the price and resolution of the image on the results analysis and comparison [9,18,19]. There are mainly two kinds of marine aquaculture areas: raft culture area (RCA) and cage culture area (CCA) [20]. The size of these marine aquaculture areas ranges from a few meters to tens of meters.…”

Section: Introductionmentioning

confidence: 99%

The Assessment of More Suitable Image Spatial Resolutions for Offshore Aquaculture Areas Automatic Monitoring Based on Coupled NDWI and Mask R-CNN

et al. 2022

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Wide-scale automatic monitoring based on the Normalized Difference Water Index (NDWI) and Mask Region-based Convolutional Neural Network (Mask R-CNN) with remote sensing images is of great significance for the management of aquaculture areas. However, different spatial resolutions brought different cost and model performance. To find more suitable image spatial resolutions for automatic monitoring offshore aquaculture areas, seven different resolution remote sensing images in the Sandu’ao area of China, from 2 m, 4 m, to 50 m, were compared. Results showed that the remote sensing images with a resolution of 15 m and above can achieve the corresponding recognition effect when no financial issues were considered, with the F1 score of over 0.75. By establishing a cost-effectiveness evaluation formula that comprehensively considers image price and recognition effect, the best image resolution in different scenes can be found, thus providing the most appropriate data scheme for the automatic monitoring of offshore aquaculture areas.

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Section: Introductionmentioning

confidence: 99%

The Assessment of More Suitable Image Spatial Resolutions for Offshore Aquaculture Areas Automatic Monitoring Based on Coupled NDWI and Mask R-CNN

et al. 2022

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“…Therefore, conducting a large-scale statistical analysis of offshore raft culture areas is particularly necessary. When engaging in scientific planning of aquaculture areas to achieve sustainable and healthy development, the collection and analysis of relevant regional data connecting aquaculture can help mitigate and defend against natural disasters [17].…”

Section: Introductionmentioning

confidence: 99%

“…The most commonly used accuracy evaluations include accuracy rate, false alarm 359 rate, and missed alarm rate. Equations ( 9)- (11) were used to evaluate the accuracy [17]:…”

mentioning

confidence: 99%

Detection and Statistics of Offshore Aquaculture Rafts in Coastal Waters

Zhou

Wong

Tsou

et al. 2022

JMSE

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Offshore aquaculture is critical for a marine fishery economy. The spatial distribution of aquaculture that characterizes it plays a vital role in the sustainable development of marine resources and the protection of the marine environment. In recent years, China’s aquaculture has developed rapidly; specifically, the scale of aquaculture has dramatically expanded, and large-scale aquaculture has gradually grown in popularity. Although high-resolution satellite data can accurately extract aquaculture areas, the extraction of a large area of the sea area requires a copious amount of data. In contrast, medium-resolution satellite images allow for the extraction of aquaculture areas from large sea areas with a smaller amount of data, offering significant advantages. Therefore, we used Landsat8 satellite data to extract and count the number of aquaculture rafts based on the Hough transform and Canny edge detection methods. We tested the accuracy of this method by selecting Haizhou Bay as the study area for the experiment and accuracy verification and found that the automatic extraction accuracy for the number of aquaculture rafts was more than 90%. Additionally, we calculated statistics on the number of aquaculture rafts in Haizhou Bay over the past seven years. The findings presented in this paper offer a significant reference value for local marine utilization, marine environment protection, and marine disaster prevention and mitigation.

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“…Combining target detection with image segmentation techniques extracts coastal nets from highresolution optical images, and then the net area is evaluated [33] . Weakly supervised semantic segmentation network extracts offshore farms from multimodal high-resolution optical remote sensing images [34] . In the last two years, some researchers have proposed to combine SAR images imaging mechanism and deep learning methods to extract offshore farms from high resolution SAR images.…”

Section: Introductionmentioning

confidence: 99%

U_EFF_NET: High-Precision Segmentation of Offshore Farms From High-Resolution SAR Remote Sensing Images

Qin

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Offshore aquaculture promotes the development of aquaculture industry and brings huge economic benefits to fishermen, while seriously affecting the near-coast environment. Accurate access to the range of offshore farms at home and abroad is of great significance for marine disaster warning and coastal management. Remote sensing is a very effective means of observing offshore farms. Offshore farms segmentation technology is more mature in high-resolution optical images. SAR images have the advantage of being available all day and all night. Using SAR images to extract offshore farms has become a recent research hotspot. Gaofen-3 and HISEA-1 satellites have high-resolution marine observation capabilities. Combined with their super observation capabilities, this paper proposes a technical solution to extract offshore farms using Gaofen-3 and HISEA-1 imags. Firstly, the combine-crop component module is proposed to create a new SAR images dataset based the original dataset. Next, common data augmentation methods such as flip, rotate, and copy-paste are used, the online multi-scale resolution image expansion is proposed to enrich the dataset and enlarge the data volume. Finally, the U_EFF_NET model is proposed. The model uses encoder-decoder structure and the light-weight feature extraction network EfficientNet-b0 as backbone. The decoder embeds the attention mechanism. The optimization strategy uses joint loss function to achieve multi-task learning, and the OHEM strategy is added to the joint loss function. The method proposed in this paper has high accuracy and high practicality as the frequency-weight intersection ratio of the extracted results on the test set is 98.12%. And the inference time is at the forefront.

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Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images—Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

Cited by 20 publications

References 19 publications

The Assessment of More Suitable Image Spatial Resolutions for Offshore Aquaculture Areas Automatic Monitoring Based on Coupled NDWI and Mask R-CNN

The Assessment of More Suitable Image Spatial Resolutions for Offshore Aquaculture Areas Automatic Monitoring Based on Coupled NDWI and Mask R-CNN

Detection and Statistics of Offshore Aquaculture Rafts in Coastal Waters

U_EFF_NET: High-Precision Segmentation of Offshore Farms From High-Resolution SAR Remote Sensing Images

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