Marine Floating Raft Aquaculture Detection of GF-3 PolSAR Images Based on Collective Multikernel Fuzzy Clustering

Fan, Jianchao; Zhao, Jun; An, Wei; Hu, Yuanyuan

doi:10.1109/jstars.2019.2910786

Cited by 29 publications

(17 citation statements)

References 48 publications

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“…Therefore, the greyscale representation of the raft aquaculture area in a SAR image consists mainly of the interaction between the sea surface and the floats. The backwards scattering of raft aquaculture consists mainly of scattering from the surface of the floats and seawater, scattering from the dihedral angle of the seawater floats, and scattering from the spirals of the seawater floats [18,25], as shown in Figure 1b. Thus, the DN values of the marine raft aquaculture area on Sentinel-1 images characterize the mixed image properties, which are highly disturbed by the seawater background.…”

Section: Scattering Characteristicsmentioning

confidence: 99%

“…Fan's team has performed much research on the extraction of marine floating raft image areas from high-resolution SAR images. Fan et al [18] analyzed the imaging characteristics of floating rafts in SAR images and integrated multisource SAR features of marine raft aquaculture areas to achieve extraction. Geng et al [19] proposed a joint sparse representation classification method to extract marine raft aquaculture areas from high-resolution SAR images.…”

Section: Introductionmentioning

confidence: 99%

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Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

Zhang

Wang

2022

Remote Sensing

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Large-scale and periodic remote sensing monitoring of marine raft aquaculture areas is significant for scientific planning of their layout and for promoting sustainable development of marine ecology. Synthetic aperture radar (SAR) is an important tool for stable monitoring of marine raft aquaculture areas since it is all-weather, all-day, and cloud-penetrating. However, the scattering signal of marine raft aquaculture areas is affected by speckle noise and sea state, so their features in SAR images are complex. Thus, it is challenging to extract marine raft aquaculture areas from SAR images. In this paper, we propose a method to extract marine raft aquaculture areas from Sentinel-1 images based on the analysis of the features for marine raft aquaculture areas. First, the data are preprocessed using multitemporal phase synthesis to weaken the noise interference, enhance the signal of marine raft aquaculture areas, and improve the significance of the characteristics of raft aquaculture areas. Second, the geometric features of the marine raft aquaculture area are combined to design the model structure and introduce the shape constraint module, which adds a priori knowledge to guide the model convergence direction during the training process. Experiments verify that the method outperforms the popular semantic segmentation model with an F1 of 84.52%.

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Section: Scattering Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

Zhang

Wang

2022

Remote Sensing

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“…At present, the primary remote sensing data sources used to identify the mariculture area are multispectral satellite remote sensing images and microwave remote sensing images [7]. The multispectral satellite remote sensing images mainly include Spot, GF-1, GF-2, and Landsat [4,[8][9][10][11]; while microwave remote sensing images mainly include Radarsat-2, GF-3, Sentinel-1, and Sentinel-2 [12][13][14][15][16][17]. The spatial resolution of diverse remote sensing data sources is quite different, and the prices and effects they can achieve are naturally different.…”

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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“…Considering data sources, most studies used only optical images for they are visually intuitive and easy to be understood. For mapping aquaculture facilities over small areas, high spatial resolution remote sensing images are frequently applied [26,27], whereas medium resolution images are generally used for mapping aquaculture facilities at a regional or national scale due to their wide coverage and better spectral resolution. For example, Ren et al [28] combined Landsat series images and an object-based classification method to map the spatiotemporal distribution of aquaculture ponds in China's coastal zone.…”

Section: Introductionmentioning

confidence: 99%

Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Zhang

et al. 2021

Remote Sensing

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Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.

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Marine Floating Raft Aquaculture Detection of GF-3 PolSAR Images Based on Collective Multikernel Fuzzy Clustering

Cited by 29 publications

References 48 publications

Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery

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

Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

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