Coastal Oyster Aquaculture Area Extraction and Nutrient Loading Estimation Using a GF-2 Satellite Image

Zhang, Xiaoyu; Ma, Shuchang; Su, Cheng; Shang, Yang; Wang, Tinggang; Yin, Jianwei

doi:10.1109/jstars.2020.3016823

Cited by 18 publications

(9 citation statements)

References 8 publications

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“…Shellfish and algae are the two main products of FRA; the former mainly includes oysters, scallops, clams, mussels, etc., while the latter mainly includes seaweed, 2 of 17 nori, etc. The FRA is widely distributed in several countries around the world, such as China [6,7], Japan [8], France [9], and the United States [10], and has contributed greatly to the local marine product acquisition and fisheries economy. The traditional on-site investigation is laborious and time consuming, with low efficiency for large-scale FRA monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional on-site investigation is laborious and time consuming, with low efficiency for large-scale FRA monitoring. Therefore, remote sensing (RS) technology is considered an effective approach because of its advantages in terms of wide spatial coverage, short revisit period, and high economic efficiency [6,[11][12][13]. Various remote-sensing-based methods have been applied for FRA extraction, including the visual interpretation method [7,14], the spectral featurebased method [15], the polarization feature-based method [16,17], and others.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extraction of Floating Raft Aquaculture Areas from Sentinel-1 SAR Images by a Dense Residual U-Net Model with Pre-Trained Resnet34 as the Encoder

et al. 2022

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Marine floating raft aquaculture (FRA) monitoring is significant for marine ecological environment and food security assessment. Synthetic aperture radar-based monitoring is considered to be an effective means of FRA identification because of its capability for all-weather applications. Considering the poor generalization and extraction accuracy of traditional monitoring methods, a semantic segmentation model called D-ResUnet is proposed to extract FRA areas from Sentinel-1 images. The proposed model has a U-Net-like structure but combines the pre-trained ResNet34 as the encoder and adds dense residual units into the decoder. For this model, the final layer and cropping operation of the original U-Net model are removed to eliminate the model parameters. The mean and standard deviation of Precision, Recall, Intersection over Union (IoU), and F1 score are calculated under a five-fold training strategy to evaluate the model accuracy. The test experiments indicated that the proposed model performs well with the F1 of 92.6% and IoU of 86.24% in FRA extraction tasks. In particular, the ablation experiments and application experiments proved the effectiveness of the improvement strategy and the portability of the proposed D-ResUnet model, respectively. Compared with the other three state-of-the-art semantic segmentation models, the experiments demonstrate a clear accuracy advantage of the D-ResUnet model. For the FRA extraction task, this paper presents a promising approach that has refined extraction capability, high accuracy, and acceptable model complexity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction of Floating Raft Aquaculture Areas from Sentinel-1 SAR Images by a Dense Residual U-Net Model with Pre-Trained Resnet34 as the Encoder

et al. 2022

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“…Visual interpretation is a classical method used to extract aquaculture areas from remote sensing images [7][8][9][10], but its operation is subjective, and the interpreter's prior knowledge has a significant influence on extraction accuracy, resulting in low efficiency. Using spectral features, some scholars have tried to automatically extract aquaculture areas according to spectral classification [11][12][13][14][15][16][17][18], or by constructing a spectral feature index [19][20][21][22][23]. Spectral classification, however, is susceptible to the phenomenon of "same object different spectrum" and "foreign object in the same spectrum", and the spectral characteristic index is constructed primarily for a certain region or a certain sensor, which has low portability and robustness.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Aquaculture Pond Region in Coastal Waters of Southeast China Based on Spectral Features and Spatial Convolution

Wang

Zhang

et al. 2022

Water

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To control the negative effects resulting from the disorderly development of aquaculture ponds and promote the development of the aquaculture industry, rapid and accurate identification and extraction techniques are essential. An aquaculture pond is a special net-like water body divided by complex roads and dikes. Simple spectral features or spatial texture features are not sufficient to accurately extract it, and the mixed feature rule set is more demanding on computer performance. Supported by the GEE platform, and using the Landsat satellite data set and corresponding DEM combined with field survey data, we constructed a decision-making model for the extraction of aquaculture ponds in the coastal waters, and applied this method to the coastal waters of Southeast China. This method combined the image spectral information, spatial features, and morphological operations. The results showed that the total accuracy of this method was 93%, and the Kappa coefficient was 0.86. The overlapping proportions of results between the automated extraction and visual interpretation for test areas were all more than 90%, and the average was 92.5%, which reflected the high precision and reliability of this extraction method. Furthermore, in 2020, the total area of coastal aquaculture ponds in the study area was 6348.51 km2, which was distributed primarily in the cities of Guangdong and Jiangsu. Kernel density analysis suggested that aquaculture ponds in Guangdong and Jiangsu had the highest degree of concentration, which means that they face higher regulatory pressure in the management of aquaculture ponds than other provinces. Therefore, this method can be used to extract aquaculture ponds in coastal waters of the world, and holds great significance to promote the orderly management and scientific development of fishery aquaculture.

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“…Therefore, monitoring the extent of aquaculture areas is a matter of great urgency. Zhang et al used GF-2 satellite images to achieve a superior extraction of aquaculture areas in turbid waters, providing technical support to the aquaculture industry [9]. However, the existence of visual interpretation relies on the visual interpreter's own experience, which is not conducive to the monitoring needs of aquaculture areas because of the large and time-consuming workload involved.…”

Section: Introductionmentioning

confidence: 99%

Harbor Aquaculture Area Extraction Aided with an Integration-Enhanced Gradient Descent Algorithm

Zhong

Liao

et al. 2021

Remote Sensing

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In this study, the harbor aquaculture area tested is Zhanjiang coast, and for the remote sensing data, we use images from the GaoFen-1 satellite. In order to achieve a superior extraction performance, we propose the use of an integration-enhanced gradient descent (IEGD) algorithm. The key idea of this algorithm is to add an integration gradient term on the basis of the gradient descent (GD) algorithm to obtain high-precision extraction of the harbor aquaculture area. To evaluate the extraction performance of the proposed IEGD algorithm, comparative experiments were performed using three supervised classification methods: the neural network method, the support vector machine method, and the maximum likelihood method. From the results extracted, we found that the overall accuracy and F-score of the proposed IEGD algorithm for the overall performance were 0.9538 and 0.9541, meaning that the IEGD algorithm outperformed the three comparison algorithms. Both the visualized and quantitative results demonstrate the high precision of the proposed IEGD algorithm aided with the CEM scheme for the harbor aquaculture area extraction. These results confirm the effectiveness and practicality of the proposed IEGD algorithm in harbor aquaculture area extraction from GF-1 satellite data. Added to that, the proposed IEGD algorithm can improve the extraction accuracy of large-scale images and be employed for the extraction of various aquaculture areas. Given that the IEGD algorithm is a type of supervised classification algorithm, it relies heavily on the spectral feature information of the aquaculture object. For this reason, if the spectral feature information of the region of interest is not selected properly, the extraction performance of the overall aquaculture area will be extremely reduced.

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Coastal Oyster Aquaculture Area Extraction and Nutrient Loading Estimation Using a GF-2 Satellite Image

Cited by 18 publications

References 8 publications

Extraction of Floating Raft Aquaculture Areas from Sentinel-1 SAR Images by a Dense Residual U-Net Model with Pre-Trained Resnet34 as the Encoder

Extraction of Floating Raft Aquaculture Areas from Sentinel-1 SAR Images by a Dense Residual U-Net Model with Pre-Trained Resnet34 as the Encoder

Extraction of Aquaculture Pond Region in Coastal Waters of Southeast China Based on Spectral Features and Spatial Convolution

Harbor Aquaculture Area Extraction Aided with an Integration-Enhanced Gradient Descent Algorithm

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