Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods.

Cyanobacteria occupy an extraordinarily diverse array of ecological niches in coral reefs because they play multifaceted roles, including primary carbon and nitrogen fixation, calcification, nutrient cycling, and oxygen production, as well as coral reef degradation through skeletal biocorrosion and polymicrobial diseases. In this study, cyanobacterial diversity in sediment, water, and coral tissues were explored in relation to coral health status (slightly, moderately, and severely damaged) of coral reefs at Weizhou Island, South China Sea. Microscopy of taxa morphological characteristics was combined with 16S rRNA gene metabarcoding. Fifteen and forty-three cyanobacterial genera were identified based on universal prokaryotic 16S rRNA gene primers and cyanobacteria-specific 16S rRNA gene primers metabarcoding, respectively, indicating a more sophisticated efficiency of the latter. In addition, three out of seven cyanobacterial strains that were isolated and identified based on morphology and phylogeny could not be detected using either molecular method. Therefore, culture-based combined cyanobacteria-specific 16S rRNA gene metabarcoding are highly recommended in future routine surveys. There was a clear distinction in cyanobacterial assemblage composition among locations with different coral health statuses, with degraded reefs exhibiting approximately a 1.25-fold increase in species compared to healthy habitats. In addition, the spreading of potentially toxic cyanobacteria, such as Nostoc and Lyngbya, in the degraded reef implies putative links to reef degradation. This study provides novel insights into the taxonomical diversity of cyanobacteria in tropical coral reefs. Metabarcoding is recommended as an effective tool for revealing cyanobacterial diversity patterns and thereby providing critical information for the effective management of coral reef ecosystems.

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