Images Of Sea Surface Research Articles

Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks

The article describes the use of deep neural networks to detect small floating objects located in a vessel’s path. The research aimed to evaluate the performance of deep neural networks by classifying sea surface images and assigning the level of threat resulting from the detection of objects floating on the water, such as fishing nets, plastic debris, or buoys. Such a solution could function as a decision support system capable of detecting and informing the watch officer or helmsman about possible threats and reducing the risk of overlooking them at a critical moment. Several neural network structures were compared to find the most efficient solution, taking into account the speed and efficiency of network training and its performance during testing. Additional time measurements have been made to test the real-time capabilities of the system. The research results confirm that it is possible to create a practical lightweight detection system with convolutional neural networks that calculates safety level in real time.

Vector analysis of unmanned aircraft sea surface imaging characterization based on ISAR

Vector analysis of unmanned aircraft sea surface imaging characterization based on ISAR

On estimating the size of informative fragments in the sea surface images

The paper proposes a solution to one of the problems arising in the construction of modern traffic safety systems in marine areas, namely, estimating the size of informative fragments in the image, which it seems advisable to use when detecting foreign objects on the sea surface image. It is proposed to estimate the size of informative fragments based on calculating the average distance between the contours of the wave elements visible in the image, such as their crests, depressions, etc. The contours of these wave elements are determined based on the Canny operator. The estimation of the sizes of informative fragments is performed along the columns and rows of the analyzed image. Computational experiments have been carried out to illustrate the developed algorithm efficiency. The obtained estimates of the sizes of informative fragments of sea surface images seem appropriate to use in their analysis, in particular, when solving problems of detecting foreign objects on sea surface images.

Real Aperture Radar Super-Resolution Imaging for Sea Surface Monitoring Based on a Hybrid Model

In recent years, super-resolution imaging techniques have been intensely introduced to enhance the azimuth resolution of real aperture scanning radar (RASR). However, there is a paucity of research on the subject of sea surface imaging with small incident angles for complex scenarios. This research endeavors to explore super-resolution imaging for sea surface monitoring, with a specific emphasis on grounded or shipborne platforms. To tackle the inescapable interference of sea clutter, it was segregated from the imaging objects and was modeled alongside I/Q channel noise within the maximum likelihood framework, thus mitigating clutter’s impact. Simultaneously, for characterizing the non-stationary regions of the monitoring scene, we harnessed the Markov random field (MRF) model for its two-dimensional (2D) spatial representational capacity, augmented by a quadratic term to bolster outlier resilience. Subsequently, the maximum a posteriori (MAP) criterion was employed to unite the ML function with the statistical model regarding imaging scene. This hybrid model forms the core of our super-resolution methodology. Finally, a fast iterative threshold shrinkage method was applied to solve this objective function, yielding stable estimates of the monitored scene. Through the validation of simulation and real data experiments, the superiority of the proposed approach in recovering the monitoring scenes and clutter suppression has been verified.

Long-Distance Sea Wave Sparse Matching Algorithm for Sea Level Monitoring System

Benefiting from rich agricultural land, easy transport and fishing, etc., more and more people are moving to live in coastal areas, with more than 200 million people now living in coastal areas that are vulnerable to extreme sea level events. Sea level information is useful for coastal societies. Image measurement is rapidly developing as a new type of measurement tool. A multicamera-based sea level monitoring system along Japan’s coast near the Pacific is proposed in this paper, and a long-distance sea wave matching method for this system is described. The whole system employs multiple binocular vision systems to take sea surface images and obtain the sea level height based on the disparity between the field of views of the left and right cameras, forming a local measurement and overall analysis monitoring system. Sea level monitoring requires a high processing accuracy and speed to realize a timely response to extreme events. Thus, the paper extracts sea waves and integrates a sea wave’s appearance features as feature points and descriptors and pioneers the idea of searching deterministic features for fast image processing. The average stereo matching precision of the proposed method is up to 89.9% with a running time smaller than 40 ms for most pairs of images. Experiments on various real sea surface image pairs are conducted to validate the effectiveness of the proposed method.

ОБНАРУЖЕНИЕ ОБЪЕКТОВ НА ОСНОВЕ ВЗАИМНОЙ КОРРЕЛЯЦИИ ФРАГМЕНТОВ ВИДЕО КАДРОВ МОРСКОЙ ПОВЕРХНОСТИ

The work is devoted to the development of a method for the objects detecting on an agitated sea surface video based on the analysis of the differences in the variability of the object and the sea surface image fragments on the neighboring frames. The proposed method does not use data about the object size, its shape, brightness, etc. The decision function has been developed that can be used to estimate the variability of a given frames fragment, based on the normalized cross-correlation coefficients values of the corresponding fragments on a video subsequent frames. The decision rule has been developed based on the proposed decision function, in which we use the threshold value (the critical domain boundary) determined at the training stage when analyzing the frames sequence fragments containing only the agitated sea surface image. The efficiency of the developed objects detection method on the agitated sea surface is demonstrated based on computational experiments. The values of the decision function critical domain boundary obtained at the training stage and the corresponding values of the type II error probability at the detection stage are given. The presented computational experiments results demonstrate that the developed method makes it possible to detect the object on video frames with the type II error probability equal to zero.

Improved CycleGAN-based shadow estimation for ocean wave height inversion from marine X-band radar images

A novel algorithm is developed to estimate the shadowing ratio for the significant wave height (SWH) inversion of the ocean wave fields imaged by horizontal polarized X-band nautical radar intelligently and conveniently. To solve the problem that the accuracy of the calculated ratio of shadowing in local image areas is not ideal, and the high resolution radar images will lead to time-consuming in estimation of root mean square slope and angle-blurred for sea surface image edge detection, a shadow estimation model from marine X-band radar images based on Convolutional Neural Network (CNN) is established. The model applies the improved CycleGAN to SWH estimation using the geometric shadow effect, which is visible on the marine X-band radar sea surface images due to the presence of the modulation effect of the rough surface. The neural network model can be successfully trained from simulation-based data and then applied to real measured data, and the algorithm does not require any reference measurements. Compared with the traditional shadow-based method, the SWH derived by using this proposed method matches well with that measured by an in-situ buoy nearby, which indicates the goodness of our proposal.

Interference image registration combined by enhanced scale-invariant feature transform characteristics and correlation coefficient

To realize the digital elevation inversion of the interferometric imaging radar altimeter (InIRA), an interference complex images registration algorithm combining enhanced scale-invariant feature transform (SIFT) characteristics with correlation coefficient is proposed. First, the locally tuned nonlinear method is used to enhance the image features. Then, SIFT algorithm is used to extract the matched feature points that are used as control points after screening. Based on these control points, the affine transformation is applied to calculate the coarse matching relation. Second, multiple control points are chosen uniformly. The local accurate offsets are determined by interpolating and calculating the maximum of correlation coefficients. The least-squares method is used to fit the difference between the two images. Third, the two images are matched by interpolating and resampling the one to be registered. Finally, the simulated InIRA sea surface images and the Sentinel-1A images of the Mount Hua area are employed to experiment. The results show that the proposed algorithm combines the advantages of SIFT algorithm and correlation coefficient algorithm. It is robust and its registration accuracy is better than the particle swarm optimization sample consensus algorithm, unsupervised deep-learning algorithm, SIFT algorithm, and correlation coefficient algorithm.

Radar sensing of the sea surface using small spacecraft

The possibility of using a constellation of small spacecraft as receiving satellites, when “highlighting” the sea surface from existing (navigational, communication) or specially created spacecraft—to form a wide-area (about 1 000 km) radar survey zone at a given resolution (about 10 m)—is under consideration. Such a constellation could provide operational monitoring of fast-moving atmospheric cyclones, measuring directly the parameters of storm waves (altitude and orbital velocity)—which would replace the existing constellation of microwave scatterometers, providing operational monitoring of the World Ocean surface in the 3 H (H—altitude of the satellite’s orbit) field of view with a resolution of about 10 km—but with calibration of the received images by wind speed and direction, which leads to huge errors when trying to introduce altitude calibration in the Small spacecraft have many advantages over large satellites. For example, they are relatively inexpensive to build, take minimal time from design to launch, are easily modified to solve a specific problem, and create less radio interference. The approach under consideration consists in redistribution of tasks to be solved between the constellation of satellites in orbit. High orbiting navigation satellites, for example, can be used as transmitter carriers (of the illumination of the surface) that use the necessary broadband signal with acceptable periodicity. Receivers of reflected signals are placed on board small spacecraft, and at formation of wide-band radar image of sea surface with necessary resolution ∼10 m (that only on order exceeds acceptable on small spacecraft size of receiving antennas)—in flight direction is necessary to use synthesized aperture of receiving antenna. This work has the character of “staged” research.

Spectral Anomaly Detection Based on Dictionary Learning for Sea Surfaces

Anomalies in remote sensing images are generally reflected in two aspects of spatial and spectral ones, as for the anomaly detection of sea surface using multispectral or hyperspectral images, spectral information is more important. To this end, a novel spectral anomaly detection method based on dictionary optimization is proposed in this letter. More specifically, the normal scene is first defined to distinguish the anomaly. Then, without any assumption about the distribution of anomaly, a spectral dictionary is formulated and derived theoretically with optimization to express the normal scenes. Using the sparse and low-rank constraints, the alternating direction method of multiplier (ADMM) is employed to solve the above optimization in the spectral domain. Finally, for a given sea-surface image to be detected, the error matrix that cannot be fully expressed by the optimized spectral dictionary is regarded as anomalies. It shows certain generality for various kinds of spectral anomalies on the sea surface. Taking multispectral images obtained by the HY-1C satellite as an example, comparisons with related state-of-the-art methods demonstrate that our proposed method achieves the best anomaly detection performance not only for oil-spill pollution but also for algae pollution.

A Stereo Matching Method for 3D Image Measurement of Long-Distance Sea Surface

Tsunamis are some of the most destructive natural disasters. Some proposed tsunami measurement and arrival prediction systems use a limited number of instruments, then judge the occurrence of the tsunami, forecast its arrival time, location and scale. Since there are a limited number of measurement instruments, there is a possibility that large prediction errors will occur. In order to solve this problem, a long-distance tsunami measurement system based on the binocular stereo vision principle is proposed in this paper. The measuring range is 4–20 km away from the system deployment site. In this paper, we will focus on describing the stereo matching method for the proposed system. This paper proposes a two-step matching method. It first performs fast sparse matching, and then complete high precision dense matching based on the results of the sparse matching. A matching descriptor based on the physical features of sea waves is proposed to solve the matching difficulty caused by the similarity of sea surface image textures. The relationship between disparity and the y coordinate is built to reduce the matching search range. Experiments were conducted on sea surface images with different shooting times and distances; the results verify the effectiveness of the presented method.

Sea-surface object detection scheme for USV under foggy environment

Sea-surface target detection is investigated for the visual image-based autonomous control of an Unmanned Surface Vessel (USV). A traditional way is to dehaze for sea-surface images in the previous target detection algorithms. However, it would cause a problem that the image dehaze performance and detection speed are difficult to be balanced. To solve the above problem, a YOLO (You Only Look Once) based target detection network with good anti-fog ability is proposed for sea-surface target detection. In this proposed method, the target detection network is trained off-line to obtain a good anti-fog ability and the target detection is performed on-line. A hazed sample generation model is built based on atmospheric single scattering inverse model to obtain sufficient samples for the off-line training in the proposed method. And then, the target detection network is trained based on the generated samples to obtain good anti-fog ability according to a new learning strategy. Finally, comparative experimental results demonstrate the effectiveness of the proposed target detection algorithm.

Correlation Properties of Sea Surface Images on Video Stream Frames

The work is devoted to the characteristic properties analysis of the sea surface image on video stream frames, which make it possible to make a decision about the detection of object images on them in the absence of a priori information about objects, which is important in the development of navigation systems, security and surveillance systems. In this paper, it is proposed to analyze the values of the normalized cross-correlation coefficients of a given image fragment with higher dimension fragments on sequential video stream frames to identify the distinctive properties of agitated sea surface and floating objects images. The computational experiments were carried out and have shown that the analysis of the results of calculating the frame fragments cross-correlations allows us to estimate the amount of displacement and distortion of a given image fragment. The results of computational experiments demonstrate the presence of differences in the values of the corresponding cross-correlation coefficients for the sea surface images with different agitation degrees, containing and not containing an image of the object. Based on the analysis of the proposed correlation properties of the sea surface images, a decisive rule for selecting fragments of frames containing an image of an object is formulated, the use of which, in many cases, allows to detect fragments of object images correctly.

UASea: A Data Acquisition Toolbox for Improving Marine Habitat Mapping

Unmanned aerial systems (UAS) are widely used in the acquisition of high-resolution information in the marine environment. Although the potential applications of UAS in marine habitat mapping are constantly increasing, many limitations need to be overcome—most of which are related to the prevalent environmental conditions—to reach efficient UAS surveys. The knowledge of the UAS limitations in marine data acquisition and the examination of the optimal flight conditions led to the development of the UASea toolbox. This study presents the UASea, a data acquisition toolbox that is developed for efficient UAS surveys in the marine environment. The UASea uses weather forecast data (i.e., wind speed, cloud cover, precipitation probability, etc.) and adaptive thresholds in a ruleset that calculates the optimal flight times in a day for the acquisition of reliable marine imagery using UAS in a given day. The toolbox provides hourly positive and negative suggestions, based on optimal or non-optimal survey conditions in a day, calculated according to the ruleset calculations. We acquired UAS images in optimal and non-optimal conditions and estimated their quality using an image quality equation. The image quality estimates are based on the criteria of sunglint presence, sea surface texture, water turbidity, and image naturalness. The overall image quality estimates were highly correlated with the suggestions of the toolbox, with a correlation coefficient of −0.84. The validation showed that 40% of the toolbox suggestions were a positive match to the images with higher quality. Therefore, we propose the optimal flight times to acquire reliable and accurate UAS imagery in the coastal environment through the UASea. The UASea contributes to proper flight planning and efficient UAS surveys by providing valuable information for mapping, monitoring, and management of the marine environment, which can be used globally in research and marine applications.

The Mono/Bistatic SAR Imaging Simulation of Sea Surface with Breaking Waves Based on a Refined Facet Scattering Field Model

In order to analyze the scattering characteristics of sea surface under high sea state, a complete scattering model of sea surface considering breaking wave is established in this study based on the refined facet scattering field model (RFSFM) and the scattering theory of breaking wave. On the basis of this model, the influence of breaking waves on the mono/bistatic SAR imaging of sea surface at HH and VV polarization is studied. The results show that with the increase in wind speed, the coverage of breaking wave increases obviously and the consideration of breaking wave has a good correction for the scattering coefficient at HH polarization under grazing incidence; however, for VV polarization, the effect of breaking wave is very small.

Single Sea Surface Image Dehazing via Multi-scale Concatenated Attention Network

Haze degrades obscures and content information of sea surface images, which can negatively impact the navigation safety of the intelligent ship in real-time systems. Therefore, it is imperative to develop algorithms that effectively remove haze and make the intelligent ship have higher environmental adaptability. In this paper, we propose a novel Multi-scale Concatenated Attention Network (MCA-Net) to address this problem. Specifically, we use the concatenation operation to connect multi-scale residual blocks (MRB), while introducing the channel attention mechanism for guiding the hazy region feature extracting. Extensive experiments on both synthetic datasets and real sea surface images, which demonstrate our proposed method outperforms are better than other recent dehazing approaches.

Spectral indexation of pixels of MODIS sea surface images for detecting inconstancy of phytopigment composition in water

This paper presents the first results of a new way of using MODIS (Moderate Resolution Imaging Spectroradiometer) sensor data to visualize phytopigment inconstancy in the near-surface layer of water basins. Other sensors of this class alike, the MODIS spectral resolution is too low to reproduce the minimums of reflectance Rrs caused by phytopigments in water. However, MODIS is remarkable for the presence of a channel at 469 nm combined with channels at 412, 443, 488, 531, 547, and 555 nm. This makes it possible to distinguish the spectral limits of preferential light absorption by chlorophyll a (412–469 nm) and by accessory pigments (469–555 nm). These capabilities were realized thanks to spectral pixel indexation (SPI) of MODIS images of the sea surface. The SPI boils down to the fact that a user determines the presence of pigment minima in spectra of every image pixel, finds the sum of the wavelengths of these minima as a WRM code and assigns it to the image pixel as one of its attributes. WRM code = 100 is assigned to pixels free of the minima. Such indexation makes it possible to examine the inconstancy of phytopigments on the background of aquatic environment variability. Application of SPI approach to MODIS images of the Gulf of Mexico and the Baltic Sea made it possible to reveal new patterns of phytopigment dynamics during HABs events.

Modeling Kelvin wake imaging mechanism of visible spectral remote sensing

Kelvin wake is an important detection target of ocean visible spectral remote sensing. In the case of slow ship speed and low atmospheric transmissivity, the wake signal is weak and the SNR (Signal-Noise Ratio) is low. Therefore, the image obtained by the visible spectral remote sensing imaging system is often “unclear”. In order to solve the problems above, this paper carries out the modeling of Kelvin wake imaging mechanism of visible spectral remote sensing. The Kelvin wake reflection distribution characteristics of rough sea surface are simulated based on the hydrodynamic model of Kelvin wake and the probability density distribution of rough sea surface, and a method to quantify the reflectance resolution for Kelvin wake imaging of rough sea surface is proposed. The changes in reflectance resolution caused by different wind speeds, ship parameters, source angle, receiver angle are also analyzed. Then the imaging link model of TDI-CCD (Time Delay and Integration Charge Coupled Devices) imaging system was established. The imaging results of a Kelvin wake reflected on the rough sea surface are simulated, and the impact of the changes of main imaging parameters (optical system F number, quantization bits, integration stages) on the imaging results of a Kelvin wake at different reflectance resolutions are analyzed. The research has clarified the mechanism of the visible spectral remote sensing system to clearly imaging the Kelvin wake, which can provide guidance on parameters design and attitude planning for the ocean visible spectral remote sensing system, and improve the ability of the imaging system to detect weak wake signals.

Improved defogging algorithm for sea surface images based on dark channel prior theory

To improve the quality of the sea surface image under foggy weather, we propose an innovative single-image defogging algorithm based on the dark channel prior principle. The median filter is combined with the minimum filter for the purpose of obtaining accurate dark channel values in areas where the depth of field changes sharply. Then, the algorithm constructs a fog density detection model to obtain the atmospheric light value from the dense fog area and effectively eliminate the interference of bright spots. Subsequently, the atmospheric scattering model is applied to obtain a preliminary fog-free image. Finally, an adaptive logarithmic mapping algorithm is introduced to enhance the visual effect of the defogged image. Experimental results show that the proposed algorithm can effectively improve image quality degradation and avoid halos in the intersection region of sky and sea, Moreover, the method does not require guided filtering for transmittance refinement, which greatly improves the execution speed of the algorithm.

Research on Multi-Frame Sea Surface Image Super Resolution Algorithm Based on Deep Learning

Research on Multi-Frame Sea Surface Image Super Resolution Algorithm Based on Deep Learning