Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Dual-Tree Complex Wavelet Input Transform for Cyst Segmentation in OCT Images Based on a Deep Learning Framework

Optical coherence tomography (OCT) represents a non-invasive, high-resolution cross-sectional imaging modality. Macular edema is the swelling of the macular region. Segmentation of fluid or cyst regions in OCT images is essential, to provide useful information for clinicians and prevent visual impairment. However, manual segmentation of fluid regions is a time-consuming and subjective procedure. Traditional and off-the-shelf deep learning methods fail to extract the exact location of the boundaries under complicated conditions, such as with high noise levels and blurred edges. Therefore, developing a tailored automatic image segmentation method that exhibits good numerical and visual performance is essential for clinical application. The dual-tree complex wavelet transform (DTCWT) can extract rich information from different orientations of image boundaries and extract details that improve OCT fluid semantic segmentation results in difficult conditions. This paper presents a comparative study of using DTCWT subbands in the segmentation of fluids. To the best of our knowledge, no previous studies have focused on the various combinations of wavelet transforms and the role of each subband in OCT cyst segmentation. In this paper, we propose a semantic segmentation composite architecture based on a novel U-net and information from DTCWT subbands. We compare different combination schemes, to take advantage of hidden information in the subbands, and demonstrate the performance of the methods under original and noise-added conditions. Dice score, Jaccard index, and qualitative results are used to assess the performance of the subbands. The combination of subbands yielded high Dice and Jaccard values, outperforming the other methods, especially in the presence of a high level of noise

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

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%

The Application of Seabed Silt in the Preparation of Artificial Algal Reefs

Large amounts of silt have been deposited on the seabed in China’s coastal areas due to intensive coastal development and marine raft aquaculture, which are the main causes of local marine environmental disasters. In this study, seabed silt was tested as a potential raw material for artificial reefs. The silt was mixed with cement in four proportions to create concrete specimens for use in silt artificial reefs (SARs). The compressive strength development and nutrient dissolution were examined in the SAR specimens. The hydration products of the SAR paste were investigated through X-ray diffraction (XRD), scanning election microscope (SEM), and differential scanning calorimetry (DSC) techniques. The results showed that the compression strength of the SAR specimens was inversely proportional to their seabed silt content. The SAR specimens were able to continuously dissolve nitrogen-containing nutrients. The presence of Ca(OH)2, commonly found in traditional concrete, was not detected, which may help improve the seaweed adhesion and biological effects of artificial reefs. The effective utilization of seabed silt could serve to restore and improve the marine ecological environment

Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images

Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and poor data of optical satellite imagery. Synthetic aperture radar (SAR) satellite imagery is currently considered to be an effective data source, while the state-of-the-art methods require manual parameter tuning under the guidance of professional experience. To preclude the limitation, this paper proposes a segmentation network combined with nonsubsampled contourlet transform (NSCT) to extract MFA areas using Sentinel-1 images. The proposed method is highlighted by several improvements based on the feature analysis of MFA. First, the NSCT was applied to enhance the contour and orientation features. Second, multiscale and asymmetric convolutions were introduced to fit the multisize and strip-like features more effectively. Third, both channel and spatial attention modules were adopted in the network architecture to overcome the problems of boundary fuzziness and area incompleteness. Experiments showed that the method can effectively extract marine raft culture areas. Although further research is needed to overcome the problem of interference caused by excessive waves, this paper provides a promising approach for periodical monitoring MFA in a large area with high efficiency and acceptable accuracy

A systems analysis of microplastic pollution in Laizhou Bay, China

Microplastic contamination is attracting increasing attention worldwide. In this study, the patterns of microplastic contamination in surface water and sediment from 58 sites, and living fish from 31 sites were investigated in a semi-dosed bay (Laizhou Bay, China). Microplastics in Laizhou Bay were pervasively distributed, particularly in the form of fibers. Microplastic abundance exhibited no significant differences among regions in either surface waters or sediments, indicating multiple sources of microplastics pollution in the bay. Spatial hotspot (Getis-Ord Gil analysis demonstrated that microplastic pollution was mainly concentrated in the LaizhouWeifang area, which in turn was mainly affected by ocean current dynamics. Although the spatial distribution of miaoplastics in sediments was different from surface water, it was also affected by geology, hydrogeology, and anthropogenic activities. The most common polymer in the surface waters was polyethylene terephthalate (PET), while cellophane (CP) was the most frequently observed polymer in sediment, suggesting different sinking behaviors of these microplastics. The proportion of low-density microplastics (PE and PP) in surface water was approximately 19.9%, but these microplastics accounted for only approximately 1.7% in the sediment, suggesting that low-density microplastic particles preferentially migrate to open sea. There were significant differences in shape, size and polymer type of the microplastics among surface water, sediment and biota (p < 0.05). Cluster analysis suggested that the Gudong, Yellow River Estuary and Laizhou-Weifang regions are three sources of microplastics, which might originate from river input, plastic recycling and marine raft aquaculture. Furthermore, microplastic particle diversity was greater in sediment at offshore sites, suggesting that these sites receive microplastics from multiple sources. Our results characterize the microplastic pollution pattern, clarify the possible transfer mechanisms between different environmental media, and will provide important information for risk evaluation and pollution control in this area. (C) 2020 Elsevier B.V. All rights reserved