Alexander Borghgraef

An evaluation of pixel-based methods for the detection of floating objects on the sea surface

Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperform the more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene.

Using graph matching to compare VHR satellite images with GIS data

We propose a graph matching methodology based on relaxation labeling to compare road junction in VHR satellite images with GIS road data. Use is made of the spatial layout between points based on the relative angle. The technique finds correspondences between set of points taking into account error on the spatial location and spurious or missing points. An analysis is given to determine the weights of the algorithm based on the expected graph error.

Statistical real-time model for performance prediction of ship detection from microsatellite electro-optical imagers

For locating maritime vessels longer than 45 meters, such vessels are required to set up an Automatic Identification System (AIS) used by vessel traffic services. However, when a boat is shutting down its AIS, there are no means to detect it in open sea. In this paper, we use Electro-Optical (EO) imagers for noncooperative vessel detection when the AIS is not operational. As compared to radar sensors, EO sensors have lower cost, lower payload, and better computational processing load. EO sensors are mounted on LEO microsatellites. We propose a real-time statistical methodology to estimate sensor Receiver Operating Characteristic (ROC) curves. It does not require the computation of the entire image received at the sensor. We then illustrate the use of this methodology to design a simple simulator that can help sensor manufacturers in optimizing the design of EO sensors for maritime applications.

The Special Case of Sea Mines

In this chapter, work carried out at the Royal Military Academy regarding sea mines and mine countermeasures is summarized. Three sensors used for the detection and identification of sea mines are studied here: sonar, gradiometer and infrared camera. These sensors can be applied to detect different types of sea mines. Some signal and image processing techniques developed to extract relevant information for the detection of underwater objects are presented in this chapter. These techniques are validated using data collected in the frame of different European and NATO projects.

Using optical flow for the detection of floating mines in IR image sequences

In the first Gulf War, unmoored floating mines proved to be a real hazard for shipping traffic. An automated system capable of detecting these and other free-floating small objects, using readily available sensors such as infra-red cameras, would prove to be a valuable mine-warfare asset, and could double as a collision avoidance mechanism, and a search-and-rescue aid. The noisy background provided by the sea surface, and occlusion by waves make it difficult to detect small floating objects using only algorithms based upon the intensity, size or shape of the target. This leads us to look at the sequence of images for temporal detection characteristics. The targets apparent motion is such a determinant, given the contrast between the bobbing motion of the floating object and the strong horizontal component present in the propagation of the wavefronts. We have applied the Proesmans optical flow algorithm to IR video footage of practice mines, in order to extract the motion characteristic and a threshold on the vertical motion characteristic is then imposed to detect the floating targets.

Optimizing the design of electro-optical micro-satellite imagers for ship detection using a real-time simulator

For locating maritime vessels longer than 45 meters, such vessels are required to set up an Automatic Identification System (AIS) used by vessel traffic services (VTS). However, when a boat is shutting down its AIS, there are no means to detect it in open sea. In this paper, we use Electro-Optical (EO) imagers for non-cooperative vessel detection when the AIS is not operational. As compared to radar sensors, EO sensors have less complex system (lower cost and lower payload) and better computational processing load. EO sensors are mounted on LEO micro-satellites. We propose a simulator providing an estimate of sensor Receiver Operating Characteristics (ROC) curves in real-time and without computing the entire image received at the sensor. This simulator can help sensor manufacturers in optimizing the design of EO sensors.

Change detection in SAR images: spatial accuracy analysis

In this work, the positional error components are studied in the process of registering SAR image data to vector data. We study the case in which only imprecise knowledge of a terrain model is available, affecting the achievable accuracy of the final registration. The available information is used to draw a spatial quality map which aids in determining the local spatial change which can reliably be measured in parts of the image.

Predicting the performance of automatic road detection

We introduce a methodology to predict the performance of road detection based on the ridge detector. The method is based on measured image statistics of the road and its immediate surroundings. It predicts the performance of the detection as well as the optimal parameter set which is needed for this detection.