In maritime accidents leading to missing persons, acquiring accurate underwater environment information is crucial for effective search and rescue (SAR) operations. Current remote sensing techniques have limitations in providing comprehensive data beneath the sea surface. This study proposes a novel technology that enhances underwater visibility estimation by incorporating numerical modeling with high-resolution satellite data. First, turbidity was calculated utilizing the 560 nm surface reflectance data from Sentinel-2. The accuracy of turbidity values was assessed by comparison with in-situ turbidity data. To delineate detailed vertical mixing patterns influencing underwater visibility distribution, a comprehensive vertical profile database for water temperature and salinity was established using the Regional Ocean Modeling System (ROMS) model. Indices of vertical mixing, as a critical factor influencing underwater visibility, was achieved through quantitative analysis of seawater temperature and salinity data. The detailed underwater visibility distance is calculated by incorporating turbidity and vertical mixing effects. Subsequently, the underwater visibility will be validated through in-situ experimental measurements of both vertical and horizontal visibility along the coast using a Secchi disk. The outcomes of this study are anticipated to significantly contribute to enhancing the efficiency of rapid search and rescue operations for missing persons at sea.

In this study, we developed a technology for detecting small objects by conducting two aerial experiments targeting various objects, including ships, mannequins, and maritime safety equipment floating in coastal areas, thereby acquiring hyperspectral image data. By utilizing the hyperspectral data, we detected the pixels corresponding to the edges of ships and employed an ellipse fitting approach to identify the vessels, achieving a length error of 0.44 m. The N-FINDR spectral unmixing technique was applied to detect lifebuoys, buoyant apparatus, and mannequins, resulting in relatively small length errors ranging from 0.08 to 0.17 m.

Hazardous and noxious substances (HNS) are being increasingly used in various industrial fields, and the transportation of these substances using ships is also continuously increasing. Unlike traditional marine accidents, HNS accidents have high-risk characteristics that can cause fatal and complex physical and environmental damage, such as flammability, explosiveness, toxicity, and corrosiveness [1] – [6] . Therefore, there is a need to develop systematic management technology that takes these HNS accident characteristics into consideration. In the event of an actual accident, the most important factor in the response is to accurately and quickly detect HNS spills to establish an optimal and effective response strategy

According to the Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances (OPRC-HNS Protocol), hazardous and noxious substances (HNS) is referred to as a chemical substance, except for oil, that impairs the ocean by destroying the environment or are harmful to humans and marine life [1] . The increase in maritime transport of HNSs from the larger and faster ships entails the potential risk of marine HNS spill accidents. Highly toxic HNSs, such as benzene, toluene, and xylene, have serious adverse effects on the human body and various organisms living in the marine ecosystems. Most colorless and transparent HNSs have limitations in visual identification and can cause secondary accidents due to explosions and fires, making human access difficult. So, HNS remote sensing based on satellites and aircraft is important for HNS detection at sea because it has the advantage of monitoring a wide area and observing it at a high resolution. To monitor this HNS spill, we designed and performed a ground HNS spill experiment using a hyperspectral sensor to detect HNS areas and estimate the spill volume. The experiment was conducted at the Centre of Documentation, Research and Experimentation on Accidental Water Pollution (CEDRE) marine pool in BREST, France. The HNS image was obtained by pouring 1 L of toluene into an outdoor marine pool and observing it with a hyperspectral sensor capable of measuring the short-wave infrared (SWIR) channel installed at a height of approximately 12 m ( Figure 1 ). At a height of 12 m, the spatial resolution of the SWIR sensor is 10.3 mm and 5.3 mm in the horizontal and vertical directions, respectively. The pure endmember spectra of toluene and seawater were extracted using principal component analysis (PCA) and N-FINDR. This method has the advantage of not requiring input variables other than the number of pure substances. And a Gaussian mixture model (GMM) was applied to the toluene abundance fraction. This model is useful when the data contain multiple Gaussian distributions. The GMM represents a normally distributed subpopulation within the entire population, and it is possible to automatically learn subpopulations without requiring data on subpopulations [2] , [3] . Considering that the histogram of the abundance fraction corresponding to all pixels constituting the HNS image is composed of two peaks containing toluene and seawater rather than a single peak, the GMM is judged to be more suitable than a single model. Empirical linear calibration based on vicarious radiometric calibration was used as an atmospheric correction method for hyperspectral images. It constructs a linear equation using the pixel value of a specific index, whose reflectance is known in advance and is applied to the total image. Rectangular-shaped target plates with white (95%), gray (50%), and black (5%) reflectance were pre-positioned for inclusion in the hyperspectral image. Consequently, a toluene area of approximately 2.4317 mm 2 was detected according to the 36% criteria suitable for HNS detection. The HNS thickness estimation was based on a three-layer two-beam interference theory model [4] . In this model, the HNS thickness was estimated based on the relationship between the reflectance and transmittance at the interface composed of three layers of air, HNS, and seawater. Because toluene has a maximum extinction coefficient of 1.3055 mm at a wavelength of 1678 nm, the closest 1676.5 nm toluene reflectance image was used for thickness estimation. Considering the detection area and ground resolution, the amount of toluene that leaked was estimated to be 0.9336 L. As the amount of toluene used in the actual ground experiment was 1 L, the accuracy of our estimation is approximately 93.36%. Although we precisely designed and constructed the HNS spill experiment and retrieved the actual amount of spill material with reasonable result, there are many possibility to affect to the estimation of HNS thickness in the complicated marine environment. One of the most important causes may be the wind speed controlling evaporation of the HNS and rapid mixing with ambient seawater. The effect of wind field on the thickness estimation should be further investigated based on more intensive experiment in both marine pool circumstances and real sea environment. In this study, the estimated total toluene volume was slightly reduced compared with the amount of toluene spilled in the experiment. The elapsed time from toluene leakage to observation with the hyperspectral sensor was up to 3 min, and there is a possibility that some toluene had evaporated or dissolved. In addition to the effect of wind speed, the evaporation of toluene is affected by other atmospheric and marine environments, such as air temperature, humidity, temperature difference between the air and sea surface temperatures, sea state including various waves. Previous studies on HNS monitoring based on remote sensing are lacking compared with those on oil spills. Considering these limitations, this study evaluated the applicability of HNS detection and thickness estimation based on oil-like reflectance spectral trends at the SWIR wavelengths. The toluene detection results based on hyperspectral remote sensing are expected to be utilized for monitoring marine HNS accidents and evaluating harmful marine ecosystem environments.

The Remotely sensed satellite or airborne images can aid rapid vessel monitoring over wide areas at high resolutions. In this study, airborne hyperspectral experiments were performed to detect marine vessels mainly including fishing boat and yacht by applying pixel-based mixture techniques and to estimate the size of the vessels through an objective ellipse fitting method. Several hyperspectral mixture algorithms, such as N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA), were used for the detection of vessels. The pixel-based probability of detection (POD) and false alarm ratio (FAR) for all 14 vessels were 96.40% and 4.30%, respectively. Compared with the digital mapping camera (DMC) images with resolutions of 0.10 m, the root-mean-square error of the length and width of the vessels were approximately 1.19 m and 0.81 m, respectively.

Hazardous Noxious Substance (HNS) is transported entirely through large vessels, so there is always a potential risk of marine HNS spills. In the event of an HNS accident, it can cause enormous human and property damage, so prompt detection is required. However, there is a limit to human access by ship, we need to use remote sensing data. In this study, ground experiments using hyperspectral cameras were performed to construct a spectral library of HNS. We classified the HNS and non-HNS by applying the hyperspectral mixture algorithm, and presented the HNS detection probability for every pixel by calculating the spectrum-based abundance fraction. The results of this study are expected to be used to estimate the extent of HNS spill in the event of a marine HNS accident.

In this study, the effect of hyperspectral atmospheric correction on accuracy of the ship detection was investigated. We applied two atmospheric correction algorithms on airborne hyperspectral data and then detected ship features from reflectance corrected by each algorithms with an unsupervised target detection method. Both ATREM and FLAASH algorithms produce comparable atmospheric correction results but the algorithm which applied with further correction options optimized for coastal monitoring purpose reveals better detection results for ship detection. Compared to the result from ATREM, the result from FLAASH-corrected reflectance shows a distinct contrast between the ship and surrounding background seawater. Mean values of spectral angles between target and endmember spectra were 0.2388 (ATREM) and 0.2169 (FLAASH). This implies a further optimization of the atmospheric correction can improve the performance of target detection using airborne hyperspectral data, especially for maritime search purpose.

The SAR-derived wind fields presented the detailed structure of wind fields along the coastal areas, which had heretofore been unobtainable from scatterometer observation. Comparison of the retrieved SAR wind speeds with in-situ buoy wind measurements showed a small difference of less than 1 m/s, which implied that the results of SAR wind retrieval satisfied the limit of accuracy of satellite scatterometry. The retrieved SAR wind fields off the east coast of Korea during August 2007 showed the distinct patterns of low wind along the coastal region. The analysis of SAR wind fields with coincided SST images indicated that these spatial distinctions of SAR wind fields were associated with the upwelling events. Based on wind and SST data from satellite data and in-situ measurements, it was found that the changes in MABL stability caused by upwelling events dominantly generated the variation of SAR wind fields at the coastal regions.

A novel linearization method is proposed for CMOS LNA design. The proposed LNA adopts two cascode FETs, one of which operates as a conventional current buffer delivering the fundamental current to the load and the other one works as a nonlinear current sinker absorbing the 3rd-order intermodulation distortion (IMD3) current generated by the common source FET. Both single-ended and differential structures are investigated at 2.14 GHz for WCDMA application. The simulation results shows that the single-ended LNA has an 18dBm IIP3 at 11.8 mW power consumption and the differential one has a 12dBm IIP3 at 24.3 mW power consumption