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Displacement of cars seen by WorldView-2 in the red and yellow band (section 800 × 400 m on A99 north of Munich)
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The focal plane assembly of most pushbroom scanner satellites is built up in a way that different multispectral or multispectral and panchromatic bands are not all acquired exactly at the same time. This effect is due to offsets of some millimeters of the CCD-lines in the focal plane. Exploiting this special configuration allows the detection of ob...
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... 0.297 ± 0.085 s as derived in Krauß et al. Near-IR2 MS2 860-1040 Recording start Recording start Coastal Blue MS2 400-450 0.008 0.008 Yellow MS2 585-625 0.008 0.016 Red-Edge MS2 705-745 0.008 0.024 Panchromatic PAN 450-800 Blue MS1 450-510 0.3 0.324 Green MS1 510-580 0.008 0.332 Red MS1 630-690 0.008 0.340 Near-IR1 MS1 770-895 0.008 0.348 (2013). Fig. 4 shows a section (800 × 400 m) of a WorldView-2 scene in the north of Munich (A99) consisting of the yellow and red band. In this image the displacement of moving cars in the two channels is clearly visible. Knowing the right-hand-traffic in Germany we see, the red band is acquired earlier than the yellow band and the image was acquired ...
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... of the yellow and red band. In this image the displacement of moving cars in the two channels is clearly visible. Knowing the right-hand-traffic in Germany we see, the red band is acquired earlier than the yellow band and the image was acquired in forward direction. Fig. 5 shows the two profiles of a car (left, also left green profile-line in fig. 4) and a large truck (right). ...
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... detect the moving objects in a first step difference images be- tween the above mentioned bands are created as shown in fig. 4. Fig. 15 shows the first step of the method where the bands in- volved are subtracted and the difference is median filtered with a merely large radius of about 18 m (9 pixels in the case of WorldView-2). In the third step the detected objects from fig. 16 (right) are fetched from the image and the nearest, best fitting (in sum of ...
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... already very good to the measurements. But between 50 and 100 km/h there can be found a bunch of outliers where the automatically derived speeds lie between 150 and 200 km/h. This is due to the missing correct detection of trucks in the images. Please refer for the explanation to the profiles shown in fig. 5 referring to the yellow-red-image in fig. 4. To solve this problem the method has to be expanded to detect trucks as continous objects before doing the ...
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Citations
... This approach has been proved to be efficient for fast moving objects, as cars. [43][44][45][46] However, in case of slow moving objects, like vessels, the time lag between bands could be insufficient to clearly detect the correspondent displacement of the object. Another issue is the difficulty to isolate the two points clusters representing ship's positions due to the presence of wake and lather generated by ship's movement. ...
... The final time lag, determined as the average of single-time lags computed for a sample of 28 vessels with known AIS speed, has been estimated in 350 ms, a value which is consistent with other studies made on different sensors. 44,46 For all the analyzed cases, the displacement due to bands time lag is higher than band-to-band co-registration error, which, for bands B2 and B4 has been estimated in 0.19 pixel. 47 The same methodology has been used for speed estimate for the second group of Sentinel-2 images. ...
Object-based image analysis approach for vessel detection on optical and radar images," Abstract. Commercial satellites for Earth observation can integrate conventional positioning and tracking systems for monitoring legal and illegal activities by sea, in order to effectively detect and prevent events threatening human life and environment. This study describes an object-oriented approach to detect vessels combining high-and medium-resolution optical and radar images. Once detected, the algorithm estimates their position, length, and heading and assigns a speed range. Tests are done using WorldView-2, QuickBird, GeoEye-1, Sentinel-2A, COSMO-SkyMed, and Sentinel-1 data imaged in several test sites including China, Australia, Italy, Hong Kong, and the western Mediterranean Sea. Validation of results with data from the automatic identification system shows that the estimates for length and heading have R 2 ¼ 0.85 and R 2 ¼ 0.92, respectively. Tests for evaluating speed from Sentinel-2 time-lag image displacement show encouraging results, with 70% of estimates' residuals within AE2 m∕s. Finally, our method is compared to the state-of-the-art search for unidentified maritime object (SUMO), provided by the European Commission's Joint Research Centre. Finally, our method is compared to the state-of-the-art SUMO. Tests with Sentinel-1 data show similar results in terms of correct detections. Nevertheless, our method returns a smaller number of false alarms compared to SUMO.
... In terms of ship activity monitoring, Liu et al. [9] pointed out that the resolution limitation and short time lag of QuickBird imagery prevents ship ghosts (caused by the time lag between the PAN and MS imagery) from being as clear as airplane ghosts. In spite of this, the target detection method proposed in [10] still recognized one ship, although its absolute speed was overestimated by about 121 km/h. Krauß [10] attributed this bad performance to the very short time gap between the acquisition of the two types of image. ...
... In spite of this, the target detection method proposed in [10] still recognized one ship, although its absolute speed was overestimated by about 121 km/h. Krauß [10] attributed this bad performance to the very short time gap between the acquisition of the two types of image. ...
The successful launch of the GF-4 satellite has greatly enhanced wide-swath and high-frequency imaging capabilities, both of which are urgently needed in the fields of ship detection and monitoring. In this letter, the applicability of GF-4 satellite data for ship recognition and maritime traffic surveillance was investigated. It is concluded that the movement of ships, the velocity and direction, for instance, can be detected by using either a single GF-4 image that has short time lags between bands, or time-series GF-4 images that have a short interval between their acquisition times, implying that GF-4 satellite images have the potentiality for the monitoring of moving ships.
... Different bands may represent different states of the ground point, especially pronounced for moving objects. This effect may cause errors in the reconstructed spectrum similar to the spatial image co-registration errors (see examples in (Krauß, 2014)). All the above mentioned factors distort the final hypercube data in a local way, meaning, that a band mismatch may appear in one part of the dataset in specific spectral bands. ...
This paper gives an overview of the new COmpact hyperSpectral Imaging (COSI) system recently developed at the Flemish Institute
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data (centimetre level) where spectral information can be correctly derived. Obtained results are comparable or better than results
reported in similar studies for an alternative system based on the Fabry–Pérot interferometer.
... Different bands may represent different states of the ground point, especially pronounced for moving objects. This effect may cause errors in the reconstructed spectrum similar to the spatial image co-registration errors (see examples in (Krauß, 2014)). All the above mentioned factors distort the final hypercube data in a local way, meaning, that a band mismatch may appear in one part of the dataset in specific spectral bands. ...
This paper gives an overview of the new COmpact hyperSpectral Imaging (COSI) system recently developed at the Flemish Institute for Technological Research (VITO, Belgium) and suitable for remotely piloted aircraft systems. A hyperspectral dataset captured from a multirotor platform over a strawberry field is presented and explored in order to assess spectral bands co-registration quality. Thanks to application of line based interference filters deposited directly on the detector wafer the COSI camera is compact and lightweight (total mass of 500g), and captures 72 narrow (FWHM: 5nm to 10 nm) bands in the spectral range of 600-900 nm. Covering the region of red edge (680 nm to 730 nm) allows for deriving plant chlorophyll content, biomass and hydric status indicators, making the camera suitable for agriculture purposes. Additionally to the orthorectified hypercube digital terrain model can be derived enabling various analyses requiring object height, e.g. plant height in vegetation growth monitoring. Geometric data quality assessment proves that the COSI camera and the dedicated data processing chain are capable to deliver very high resolution data (centimetre level) where spectral information can be correctly derived. Obtained results are comparable or better than results reported in similar studies for an alternative system based on the Fabry–Pérot interferometer.
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