Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.
A method for the determination of seventeen phthalate esters in sediment by accelerated solvent extraction (ASE), gel-permeation chromatography (GPC) and gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) has been developed. The target compounds were extracted at 100 degrees C and 103.4 MPa (1500 psi) by ASE using the mixtures of dichloromethane and acetone (1:1, v/v) as solvent. In order to eliminate the interferences from larger molecular sizes, the extract was purified at a flow rate of 5.0 mL/min by GPC. Following that, the extract was concentrated to a final volume of 1 mL exactly. The GC-MS/MS was applied to quantitative and qualitative analysis. Internal standard calibration approach was adopted, and the detection limits of seventeen phthalate esters ranging from 0.05 to 0.40 microg/kg were obtained. The correlation coefficients were beyond 0.996, the recoveries were from 50.5% to 107.9%, and the relative standard deviations were from 3.5% to 13.9%. Besides, the surrogate compounds spiked were used to monitor the performance of the method, and the recoveries were from 65.3% to 95.8% for the three surrogate compounds. The method is fast, sensitive and exact for analyzing seventeen phthalate esters simultaneously.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.