Spectral imaging in preclinical research and clinical pathology.

Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure multiple parameters at the molecular and cellular level. These tools can provide valuable insights into the basic mechanisms of life, and yield diagnostic and prognostic information for clinical applications. There are many multispectral technologies available, each with its own advantages and limitations. This chapter will present an overview of the rationale for spectral imaging, and discuss the hardware, software and sample labeling strategies that can optimize its usefulness in clinical settings

Application of advanced techniques for the remote detection, modelling and spatial analysis of mesquite (prosopis spp.) invasion in Western Australia

Invasive plants pose serious threats to economic, social and environmental interests throughout the world. Developing strategies for their management requires a range of information that is often impractical to collect from ground based surveys. In other cases, such as retrospective analyses of historical invasion rates and patterns, data is rarely, if ever, available from such surveys. Instead, historical archives of remotely sensed imagery provides one of the only existing records, and are used in this research to determine invasion rates and reconstruct invasion patterns of a ca 70 year old exotic mesquite population (Leguminoseae: Prosopis spp.) in the Pilbara Region of Western Australia, thereby helping to identify ways to reduce spread and infill. A model was then developed using this, and other, information to predict which parts of the Pilbara are most a risk. This information can assist in identifying areas requiring the most vigilant intervention and pre-emptive measures. Precise information of the location and areal extent of an invasive species is also crucial for land managers and policy makers for crafting management strategies aimed at control, confinement or eradication of some or all of the population. Therefore, the third component of this research was to develop and test high spectral and spatial resolution airborne imagery as a potential monitoring tool for tracking changes at various intervals and quantifying the effectiveness of management strategies adopted. To this end, high spatial resolution digital multispectral imagery (4 channels, 1 m spatial resolution) and hyperspectral imagery (126 channels, 3 m spatial resolution) was acquired and compared for its potential for distinguishing mesquite from coexisting species and land covers.These three modules of research are summarised hereafter. To examine the rates and patterns of mesquite invasion through space and time, canopies were extracted from a temporal series of panchromatic aerial photography over an area of 450 ha using unsupervised classification. Non-mesquite trees and shrubs were not discernible from mesquite using this imagery (or technique) and so were masked out using an image acquired prior to invasion. The accuracy of the mesquite extractions were corroborated in the field and found to be high (R2 = 0.98, P36 m2 (66-94%) with both approaches and image types. However, both approaches used on the hyperspectral imagery were more reliable at capturing patches >36 m2 than the DMSI using either approach. The lowest omission and commission rates were obtained using pairwise separation on the hyperspectral imagery, which was significantly more accurate than DMSI using an overall separation approach (Z=2.78, P36 m2. However, hyperspectral imagery processed using pairwise separation appears to be superior, even though not statistically different to hyperspectral imagery processed using overall separation or DMSI processed using pairwise separation at the 95% confidence level. Mapping smaller patches may require the use of very high spatial resolution imagery, such as that achievable from unmanned airborne vehicles, coupled with a hyperspectral instrument. Alternatively, management may continue to rely on visual airborne surveys flown at low altitude and speed, which have proven to be capable at mapping small and isolated mesquite shrubs in the study area used in this research

Dimensionality reduction using parallel ICA and its implementation on FPGA in hyperspectral image analysis

Hyperspectral images, although providing abundant information of the object, also bring high computational burden to data processing. This thesis studies the challenging problem of dimensionality reduction in Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension: band selection and feature extraction. This thesis presents a band selection technique based on Independent Component Analysis (ICA), an unsupervised signal separation algorithm. Given only the observations of hyperspectral images, the ICA –based band selection picks the independent bands which contain most of the spectral information of the original images. Due to the high volume of hyperspectral images, ICA -based band selection is a time consuming process. This thesis develops a parallel ICA algorithm which divides the decorrelation process into internal decorrelation and external decorrelation such that computation burden can be distributed from single processor to multiple processors, and the ICA process can be run in a parallel mode. Hardware implementation is always a faster and real -time solution to HSI analysis. Until now, there are few hardware designs for ICA -related processes. This thesis synthesizes the parallel ICA -based band selection on Field Programmable Gate Array (FPGA), which is the best choice for moderate designs and fast implementations. Compared to other design syntheses, the synthesis present in this thesis develops three ICA re-configurable components for the purpose of reusability. In addition, this thesis demonstrates the relationship between the design and the capacity utilization of a single FPGA, then discusses the features of High Performance Reconfigurable Computing (HPRC) to accomodate large capacity and design requirements. Experiments are conducted on three data sets obtained from different sources. Experimental results show the effectiveness of the proposed ICA -based band selection, parallel ICA and its synthesis on FPGA

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km², including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping

Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014

A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.JRC.G.2-Global security and crisis managemen

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions