Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%.

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

Dispersive acoustic metamaterials (AMMs) with graded refractive index is promising in acoustic sensing enhancement by providing acoustic rainbow trapping (ART) effect. However, previous solutions to this type of AMMs are based on 2D unit cells that require infinite or very large size in the third dimension; they cannot integrate with conventional acoustic sensors without deteriorating the ability of acoustic wave manipulation. In order to advance their practical applications, this paper proposes an axisymmetric, microphone embedded AMM device with gradient profile as well as gradient plate thickness and gradient gap width along the axis. Both numerical simulations and experiments are performed to examine the ART effect of the proposed AMM device and demonstrate its excellent capacity of acoustic pressure amplification and multi-band acoustic filtering. Deploying a microphone in the gap does not disturb the acoustic field, so the proposed AMM sensor experimentally achieves remarkable acoustic gains over one order of magnitude. Sound waves of different frequencies are picked up by microphones at different gaps, realizing the real-time spatially splitting of the sound spectrum. The proposed AMM provides excellent immunity to broadband noise and a little weak suppression of the single tone noise while significantly amplifying acoustic signals of a given frequency.

Investigation of subsidence situations is important for the design of railway lines. Traditional geological investigations are expensive. Multi-temporal InSAR (MT-InSAR) analysis has shown great potential for detecting subsidence areas along large-scale manmade linear features (LMLFs). The disadvantage of applying high-resolution SAR data such as that from TerraSAR-X and COSMO-SkyMed data is the high cost. By contrast, the free and open data from Sentinel-1 A/B offer an unprecedented opportunity for InSAR applications. Despite these advantages, research investigating the potential for monitoring subsidence along LMSFs using multi-temporal Sentinel-1 InSAR data at the design stage of LMSFs is limited. This paper explores the monitoring ability of MT-InSAR analysis with Sentinel-1 A/B multi-temporal data in an experiment along the planned Jakarta-Bandung high-speed railway in Bandung, Indonesia. Subsidence patterns along the route of the planned high-speed railway were analyzed in detail. By means of validation using 12 leveling points, the root mean square error (RMSE) of the average displacement velocity that was obtained from the MT-InSAR results was found to be 10.31 mm/yr. This paper demonstrates the potential of Sentinel-1 multi-temporal INSAR data to detect potential subsidence areas along planned LMLFs (e.g., the planned high-speed railway that is the focus of this study), serving as a reference for further planning of monitoring subsidence over planned LMLFs.

The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the successful application of traditional ultrasound for brain imaging. In this study, we propose a physics-embedded neural network with deep learning based full waveform inversion (PEN-FWI), which can achieve reliable quantitative imaging of brain tissues. The network consists of two fundamental components: forward convolutional neural network (FCNN) and inversion sub-neural network (ISNN). The FCNN explores the nonlinear mapping relationship between the brain model and the wavefield, replacing the tedious wavefield calculation process based on the finite difference method. The ISNN implements the mapping from the wavefield to the model. PEN-FWI includes three iterative steps, each embedding the FCNN into the ISNN, ultimately achieving tomography from wavefield to brain models. Simulation and laboratory tests indicate that PEN-FWI can produce high-quality imaging of the skull and soft tissues, even starting from a homogeneous water model. PEN-FWI can achieve excellent imaging of clot models with constant uniform distribution of velocity, randomly Gaussian distribution of velocity, and irregularly shaped randomly distributed velocity. Robust differentiation can also be achieved for brain slices of various tissues and skulls, resulting in high-quality imaging. The imaging time for a horizontal cross-sectional image of the brain is only 1.13 seconds. This algorithm can effectively promote ultrasound-based brain tomography and provide feasible solutions in other fields.

Synthetic aperture radar (SAR) has been widely applied in oil spill detection on the sea surface due to the advantages of wide area coverage, all-weather operation, and multi-polarization characteristics. Sentinel-1 satellites can provide dual-polarized SAR data, and they have high potential for successful application to oil spill detection. However, the characteristics of the sea surface and oil film on different images are not the same when imaging at different locations and in different conditions, which leads to the inconsistent accuracy of these images with the application of the current oil spill detection methods. In order to avoid the above limitation, we propose an oil spill detection method using image stretching based on superpixels and a convolutional neural network. Experiments were carried out on eight Sentinel-1 dual-pol data, and the optimal superpixel number and image stretching parameters are discussed. Mean intersection over union (MIoU) was used to evaluate classification accuracy. The proposed method could effectively improve the classification accuracy; when the expansion and inhibition coefficients of image stretching were set to 1.6 and 1.2 respectively, the experiments achieved a maximum MIoU of 85.4%, 7.3% higher than that without image stretching.