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.
The Fukushima Daiichi nuclear disaster that occurred on March 11, 2011 was caused by the Tohoku tsunami which was itself triggered by the devastating 9.0Mw moment magnitude earthquake. The present study investigates spatial and temporal changes of Suspended Particulate Matter (SPM) content in the NorthEastern part of Japan (Pacific Ocean) using a geostationary ocean color sensor. The Geostationary Ocean Color Imager (GOCI), which is centered on the Korean peninsula but could also observe the Japanese area, is able to acquire 8 images per day, thus allowing the analysis of rapid daily changes in water mass. The analysis of GOCI data shows that SPM concentration notably increased both along the coast and within the Bay of Sendai shortly after the tsunami. Motionless patterns of SPM were observed at 2, 14, 25 and 37 km from the coast. It is shown that SPM concentration rapidly decreased one month later. The SPM concentration did not remain high the following year, contrary to what was observed for the Sumatra Tsunami in 2004. The origin of SPM is also investigated in this study. Our analysis suggests that some of the SPM originates from the resuspension of bottom sediments due to the reflection of the tsunami on the coastline that leads to the migration of marine particles towards the sea surface. The fate of the SPM concentration is then discussed based on the analysis of meteorological conditions, river discharge and tsunami wave properties.
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