This paper presents a video-based solution for real time vehicle detection and counting system, using a surveillance camera mounted on a relatively high place to acquire the traffic video stream.The two main methods applied in this system are: the adaptive background estimation and the Gaussian shadow elimination. The former allows a robust moving detection especially in complex scenes. The latter is based on color space HSV, which is able to deal with different size and intensity shadows. After these two operations, it obtains an image with moving vehicle extracted, and then operation counting is effected by a method called virtual detector.

Foam is often present in satellite images of coastal areas and can lead to serious errors in the detection of shorelines especially when processing high spatial resolution images (<20 m). This study focuses on shoreline extraction and shoreline evolution using high spatial resolution satellite images in the presence of foam. A multispectral supervised classification technique is selected, namely the Support Vector Machine (SVM) and applied with three classes which are land, foam and water. The merging of water and foam classes followed by a segmentation procedure enables the separation of land and ocean pixels. The performance of the method is evaluated using a validation dataset acquired on two study areas (south and north of the bay of Sendaï—Japan). On each site, WorldView-2 multispectral images (eight bands, 2 m resolution) were acquired before and after the Fukushima tsunami generated by the Tohoku earthquake in 2011. The consideration of the foam class enables the false negative error to be reduced by a factor of three. The SVM method is also compared with four other classification methods, namely Euclidian Distance, Spectral Angle Mapper, Maximum Likelihood, and Neuronal Network. The SVM method appears to be the most efficient to determine the erosion and the accretion resulting from the tsunami, which are societal issues for littoral management purposes.

Open ocean and coastal area monitoring requires multispectral satellite images with a middle spatial resolution (~300 m) and a high temporal repeatability (~1 h). As no current satellite sensors have such features, the aim of this study is to propose a fusion method to merge images delivered by a low earth orbit (LEO) sensor with images delivered by a geostationary earth orbit (GEO) sensor. This fusion method, called spatial spectral temporal fusion (SSTF), is applied to the future sensors- Ocean and Land Color Instrument (OLCI) (on Sentinel-3) and Flexible Combined Imager (FCI) (on Meteosat Third Generation) whose images were simulated. The OLCI bands, acquired at t ₀ , are divided by the oversampled corresponding FCI band acquired at t ₀ and multiplied by the FCI bands acquired at t ₁ . The fusion product is used for the next fusion at t ₁ and so on. The high temporal resolution of FCI allows its signal-to-noise ratio (SNR) to be enhanced by the means of temporal filtering. The fusion quality indicator ERGAS computed between SSTF fusion products and reference images is around 0.75, once the FCI images are filtered from the noise and 1.08 before filtering. We also compared the estimation of chlorophyll (Chl), suspended particulate matter (SPM), and colored dissolved organic matter (CDOM) maps from the fusion products with the input simulation maps. The comparison shows an average relative errors on Chl, SPM, and CDOM, respectively, of 64.6%, 6.2%, and 9.5% with the SSTF method. The SSTF method was also compared with an existing fusion method called the spatial and temporal adaptive reflectance fusion model (STARFM).

The objective of this work is to simulate global images that would be provided by a theoretical ocean color sensor on a geostationary orbit at longitude 0, in order to assess the range of radiance value data reaching the sensor throughout the day for 20 spectral bands similar to those of the Ocean and Land Color Imager (OLCI). The secondary objective is to assess the illumination and viewing geometries that result in sunglint. For this purpose, we combined a radiative transfer model for ocean waters (Hydrolight) and a radiative transfer model for atmosphere (MODTRAN) to construct the simulated radiance images at the sea surface and at the Top-Of-Atmosphere (TOA). Bio-optical data from GlobColour level 3 products are used as input maps in the ocean radiance model. The first result of this study is the radiance dynamic range of the scene during the day. The second result indicates the angular limit to avoid the sun glint phenomenon ( and ), where the viewing zenith angle, the solar zenith angle and the relative azimuth angle. We have also shown that a significant signal from water is measured when the ratio is higher than 3%, i.e., when is lower than 90, with a limit of 60 for the two angles.

The monitoring of coastal areas using remote sensing techniques is an important issue to determine the bio-optical properties of the water column and the seabed composition. New hyperspectral satellite sensors (e.g., PRISMA, DESIS or EnMap) are developed to periodically observe ecosystems. The uncertainties in the retrieved geophysical products remain a key issue to release reliable data useful for the end-users. In this study, an analytical approach based on Information theory is proposed to investigate the Cramér–Rao lower Bounds (CRB) for the uncertainties in the ocean color parameters. Practically, during the inversion process, an a priori knowledge on the estimated parameters is used since their range of variation is supposed to be known. Here, a Bayesian approach is attempted to handle such a priori knowledge. A Bayesian CRB (BCRB) is derived using the Lee et al. semianalytical radiative transfer model dedicated to shallow waters. Both environmental noise and bio-optical parameters are supposed to be random vectors that follow a Gaussian distibution. The calculation of CRB and BCRB is carried out for two hyperspectral images acquired above the French mediterranean coast. The images were obtained from the recently launched hyperspectral sensors, namely the DESIS sensor (DLR Earth Sensing Imaging Spectrometer, German Aerospace Center), and PRISMA (Precursore IpperSpettrale della Mission Applicativa—ASI, Italian Space Adjency) sensor. The comparison between the usual CRB approach, the proposed BCRB approach and experimental errors obtained for the retrieved bathymetry shows the better ability of the BCRB to determine minimum error bounds.