A feature extraction method of ship-radiated noise based on mathematical morphological filtering

In order to extract feature of ship signal more effectively, we propose a new approach for mathematical morphological filtering based on the morphological features. Mathematical morphological filter is a new nonlinear filter, which can effectively extract the edge contour and shape characteristics. The stimulation signal is processed by mathematical morphological filtering of different structure elements, which confirms the effect of morphological filtering on suppressing noise and preserving the nonlinear characteristics. Using flat structure element, the measured ship-radiated noise signals are processed by average filter, and the filtered signals are analyzed on the frequency spectrum. Compared with other filters, the result shows that the mathematical morphological filtering can successfully extract the effective information from the ship-radiated noise signals.

Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAractérisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6–60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice.

Diffraction imaging using a mathematical morphological filter with a time-varying structuring element

Diffraction imaging is an important technique for high-resolution imaging because of the close relationship between diffractions and small-scale discontinuities. Therefore, we propose a diffraction imaging method using a mathematical morphological filter (MMF). In a common-image gather (CIG), reflections have an evident energy band associated with the Fresnel zone and stationary point, whereas diffractions can be observed in a wide illumination direction and therefore has no energy band. Based on these phenomena, we analyze the amplitude distributions of the diffractions and reflections, and propose a time-varying structuring element (SE) in the MMF. Based on the time-varying SE, the proposed method can effectively suppress reflections and has the advantage of automatically preserving the diffractions energy near the stationary point. Numerical and field experiments demonstrate the efficient performance of the proposed method in imaging diffractions and obtaining high-resolution information.