Modeling Envisat RA-2 waveforms in the coastal zone: case-study of calm water contamination

Radar altimeters have so far had limited use in the coastal zone, the area with most societal impact. This is due to both lack of, or insufficient accuracy in the necessary corrections, and more complicated altimeter signals. This paper examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10 km2 island in the NW Mediterranean). Forty-six repeat passes were analysed, with most showing a reduction in signal upon passing over the island, with weak early returns corresponding to the reflections from land. Intriguingly one third of cases showed an anomalously bright hyperbolic feature. This feature may be due to extremely calm waters in the Golfo della Botte (northern side of the island), but the cause of its intermittency is not clear. The modelling of waveforms in such a complex land/sea environment demonstrates the potential for sea surface height retrievals much closer to the coast than is achieved by routine processing. The long-term development of altimetric records in the coastal zone will not only improve the calibration of altimetric data with coastal tide gauges, but also greatly enhance the study of storm surges and other coastal phenomena

A preliminary experiment definition for video landmark acquisition and tracking

Six scientific objectives/experiments were derived which consisted of agriculture/forestry/range resources, land use, geology/mineral resources, water resources, marine resources and environmental surveys. Computer calculations were then made of the spectral radiance signature of each of 25 candidate targets as seen by a satellite sensor system. An imaging system capable of recognizing, acquiring and tracking specific generic type surface features was defined. A preliminary experiment definition and design of a video Landmark Acquisition and Tracking system is given. This device will search a 10-mile swath while orbiting the earth, looking for land/water interfaces such as coastlines and rivers

Laser space rendezvous and docking tradeoff

A spaceborne laser radar (LADAR) was configured to meet the requirements for rendezvous and docking with a cooperative object in synchronous orbit. The LADAR, configurated using existing pulsed CO2 laser technology and a 1980 system technology baseline, is well suited for the envisioned space tug missions. The performance of a family of candidate LADARS was analyzed. Tradeoff studies as a function of size, weight, and power consumption were carried out for maximum ranges of 50, 100, 200, and 300 nautical miles. The investigation supports the original contention that a rendezvous and docking LADAR can be constructed to offer a cost effective and reliable solution to the envisioned space missions. In fact, the CO2 ladar system offers distinct advantages over other candidate systems

A Comparison of Fixed Threshold CFAR and CNN Ship Detection Methods for S-band NovaSAR Images

NovaSAR is a commercial S-band Synthetic Aperture Radar (SAR) small satellite, built and operated by SSTL in the UK. One of its primary mission objectives is to carry out maritime surveillance and monitoring for security and defence applications. An investigation was carried out into comparing and contrasting conventional and new methods to perform automated ship detection in NovaSAR images. The outcome of this investigation could show the potential effectiveness of ship detection using spaceborne S-band SAR for Maritime Domain Awareness (MDA). The conventional approach is to apply a suitable distribution model to characterise sea surface clutter, followed by the implementation of a fixed threshold, Constant False Alarm Rate (CFAR) detection algorithm. In comparison, a RetinaNet-based convolutional neural network (CNN)solution was developed and trained on an open-source C-band dataset in order to determine the validity of applying non-native training data to S-band imagery. The detection performance was then compared with the CFAR technique, finding that for two selected test acquisitions a CNN-based ship detection algorithm was able to outperform a fixed threshold, CFAR-based method in the absence of native training data. CNN ship detection performance was further improved by applying transfer learning to a native S-band NovaSAR image dataset