Marine floating raft aquaculture is widely distributed in nearshore zones. The effective information extraction of floating raft aquaculture is conducive to the dynamic monitoring of sea area uses

which can rationally use marine aquaculture resources and create a healthy balance of sea ecological environment. Satellite synthetic aperture radar (SAR) imagery can overcome the influences of the marine meteorological environment and effectively reflect the locations of floating rafts. However

the information of floating raft aquaculture in SAR images is seriously affected by multiplicative speckle noise. Considerable isolated noise points exist on the surface of floating raft aquaculture

and the edges are so fuzzy that clearly distinguishing floating raft aquaculture from the sea background is difficult. The traditional unsupervised algorithm is ineffective for not taking the characteristics of SAR data into account. To solve the problem

this study improves the local binary pattern algorithm to generate the generalized local binary pattern (GLBP)

which reduces noise sensitivity and obtains texture features according to SAR data characteristics. The GLBP is then added to the merging criterion of the generalized statistical region merging (GSRM). A multi-feature integration model is constructed based on GLBP_GSRM to acquire superpixels with highly consistent texture features and achieve accurate extraction of the information on floating raft aquaculture. Given that SAR data are characteristics of multiplicative noise

the local binary pattern operator is improved to obtain the GLBP operator. The GLBP is then added to the merging criterion of the GSRM to raise the merging requirement and acquire superpixels. Superpixel segmentation with texture information can be used to obtain texture-consistent superpixels and effectively overcome the high contamination of speckle noise. Nonsubsampled contourlet transform (NSCT) is utilized to obtain the contour feature and enrich the data feature. The fuzzy compactness and separation algorithm is used to cluster and achieve the unsupervised extraction of floating raft aquaculture. In the experiments

the sea area near Changhai County in Liaoning Province is selected as the research area. The images of Radarsat-2 SAR with C-band and of TerraSAR with X-band are used. Different regions in the same image and the same region in different images are selected. Experimental results are compared with real results of the field survey and show that the proposed model can precisely extract information of floating raft aquaculture in SAR images with different types. Moreover

the proposed model is distinctly superior to the classical unsupervised algorithms. The classification accuracies in three experiments are 88.31%

85.02%

and 85.52%

which prove the effectiveness of the proposed model. A comparison of the experimental results of different regions in the same image indicates that

although the backscattering properties of different regions are different and the experimental data of two regions are affected by the speckle noise differently

the proposed method can still accurately and effectively extract the information of floating raft aquaculture. A comparison of the experimental results of the same region in different images with different bands and resolutions presents that

although the capturing time interval of images is several months and the floating raft aquaculture also has some changes

the backscattering characteristics of the same region are similar and seriously affected by the speckle noise. Experimental results show that the proposed model can accurately extract the floating raft aquaculture information of the same region in different SAR images with different resolutions. Some experiments also analyzed the contribution of each part of the model

and the results show that the superpixel segmentation algorithm GLBP_GSRM can obtain superpixels with high texture consistency and reduce the isolated points in the entire image. The contourlet feature obtained from the NSCT enriches the feature of the raw data and can make areas of floating raft aquaculture more complete with smooth edges. In brief

the proposed model can effectively overcome the high contamination of the speckle noise and precisely extract the information of floating raft aquaculture. The proposed model utilizes the texture feature

spatial feature

and contour feature to solve the problem of speckle noise jamming information extraction. In SAR images with different types

the proposed model can effectively extract the information of floating raft aquaculture from complex oceanic backgrounds and improve the automatic monitoring accuracy of marine aquaculture. The idea of the proposed model can be widely applied to the information extraction of other types in sea areas and make it more convenient to dynamically monitor the sea area. However

in this study

SAR images with different bands and resolutions are used in the experiments and are unable to explore the resolution influence. Therefore

the next research is to study SAR data with different resolutions in the same band and explore the resolution effect on the information extraction of floating raft aquaculture.