Case study: object and ground point separation on a river bank using airborne based LIDAR data

In the past decade, airborne based LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public sectors as a reliable and accurate source for land surveying in environmental, engineering and civil applications. Commonly, the first task to investigate LIDAR point clouds is to separate ground and object points. Skewness Balancing has been proven to be an efficient non-parametric unsupervised classification algorithm to address this challenge. Initially developed for moderate terrain, this algorithm needs to be adapted to handle sloped terrain. This paper addresses the difficulty of object and ground point separation in LIDAR data in hilly terrain. A case study on a diverse LIDAR data set in terms of data provider, resolution and LIDAR echo has been carried out. Several sites in urban and rural areas with man-made structure and vegetation in moderate and hilly terrain have been investigated and three categories have been identified. A deeper investigation on an urban scene with a river bank has been selected to extend the existing algorithm. The results show that an iterative use of Skewness Balancing is suitable for sloped terrain.

Threshold-free object and ground point separation in LIDAR data

Light Detection And Ranging (LIDAR) is an important modality in terrain and land surveying for many environmental, engineering and civil applications. This paper presents the framework for a recently developed unsupervised classification algorithm called Skewness Balancing for object and ground point separation in airborne LIDAR data. The main advantages of the algorithm are threshold-freedom and independence from LIDAR data format and resolution, while preserving object and terrain details. The framework for Skewness Balancing has been built in this contribution with a prediction model in which unknown LIDAR tiles can be categorised as “hilly” or “moderate” terrains. Accuracy assessment of the model is carried out using cross-validation with an overall accuracy of 95%. An extension to the algorithm is developed to address the overclassification issue for hilly terrain. For moderate terrain, the results show that from the classified tiles detached objects (buildings and vegetation) and attached objects (bridges and motorway junctions) are separated from bare earth (ground, roads and yards) which makes Skewness Balancing ideal to be integrated into geographic information system (GIS) software packages.

Adaptive divergence and speciation among sexual and pseudoviviparous populations of Festuca

Pseudovivipary is an environmentally induced flowering abnormality in which vegetative shoots replace seminiferous (sexual) inflorescences. Pseudovivipary is usually retained in transplantation experiments, indicating that the trait is not solely induced by the growing environment. Pseudovivipary is the defining characteristic of Festuca vivipara, and arguably the only feature separating this species from its closest seminiferous relative, Festuca ovina. We performed phylogenetic and population genetic analysis on sympatric F. ovina and F. vivipara samples to establish whether pseudovivipary is an adaptive trait that accurately defines the separation of genetically distinct Festuca species. Chloroplast and nuclear marker-based analyses revealed that variation at a geographical level can exceed that between F. vivipara and F. ovina. We deduced that F. vivipara is a recent species that frequently arises independently within F. ovina populations and has not accumulated significant genetic differentiation from its progenitor. We inferred local gene flow between the species. We identified one amplified fragment length polymorphism marker that may be linked to a pseudovivipary-related region of the genome, and several other markers provide evidence of regional local adaptation in Festuca populations. We conclude that F. vivipara can only be appropriately recognized as a morphologically and ecologically distinct species; it lacks genetic differentiation from its relatives. This is the first report of a ‘failure in normal flowering development’ that repeatedly appears to be adaptive, such that the trait responsible for species recognition constantly reappears on a local basis.

Object detecting artificial retina

This paper describes the novel use of agent and cellular neural Hopfield network techniques in the design of a self-contained, object detecting retina. The agents, which are used to detect features within an image, are trained using the Hebbian method which has been modified for the cellular architecture. The success of each agent is communicated with adjacent agents in order to verify the detection of an object. Initial work used the method to process bipolar images. This has now been extended to handle grey scale images. Simulations have demonstrated the success of the method and further work is planned in which the device is to be implemented in hardware.

Object-based three-dimensional X-ray imaging

A form of three-dimensional X-ray imaging, called Object 3-D, is introduced, where the relevant subject material is represented as discrete ‘objects’. The surface of each such object is derived accurately from the projections of its outline, and of its other discontinuities, in about ten conventional X-ray views, distributed in solid angle. This technique is suitable for many applications, and permits dramatic savings in radiation exposure and in data acquisition and manipulation. It is well matched to user-friendly interactive displays.