Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm

Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal

Using 1st derivative reflectance signatures within a remote sensing framework to identify macroalgae in marine environments

Macroalgae blooms (MABs) are a global natural hazard that are likely to increase in occurrence with climate change and increased agricultural runoff. MABs can cause major issues for indigenous species, fish farms, nuclear power stations, and tourism activities. This project focuses on the impacts of MABs on the operations of a British nuclear power station. However, the outputs and findings are also of relevance to other coastal operators with similar problems. Through the provision of an early-warning detection system for MABs, it should be possible to minimize the damaging effects and possibly avoid them altogether. Current methods based on satellite imagery cannot be used to detect low-density mobile vegetation at various water depths. This work is the first step towards providing a system that can warn a coastal operator 6–8 h prior to a marine ingress event. A fundamental component of such a warning system is the spectral reflectance properties of the problematic macroalgae species. This is necessary to optimize the detection capability for the problematic macroalgae in the marine environment. We measured the reflectance signatures of eight species of macroalgae that we sampled in the vicinity of the power station. Only wavelengths below 900 nm (700 nm for similarity percentage (SIMPER)) were analyzed, building on current methodologies. We then derived 1st derivative spectra of these eight sampled species. A multifaceted univariate and multivariate approach was used to visualize the spectral reflectance, and an analysis of similarities (ANOSIM) provided a species-level discrimination rate of 85% for all possible pairwise comparisons. A SIMPER analysis was used to detect wavebands that consistently contributed to the simultaneous discrimination of all eight sampled macroalgae species to both a group level (535–570 nm), and to a species level (570–590 nm). Sampling locations were confirmed using a fixed-wing unmanned aerial vehicle (UAV), with the collected imagery being used to produce a single orthographic image via standard photogrammetric processes. The waveband found to contribute consistently to group-level discrimination has previously been found to be associated with photosynthetic pigmentation, whereas the species-level discriminatory waveband did not share this association. This suggests that the photosynthetic pigments were not spectrally diverse enough to successfully distinguish all eight species. We suggest that future work should investigate a Charge-Coupled Device (CCD)-based sensor using the wavebands highlighted above. This should facilitate the development of a regional-scale early-warning MAB detection system using UAVs, and help inform optimum sensor filter selection.

Applied Fourier Transform Near-infrared Techniques for Biomass Compositional Analysis

A new method for rapid chemical analysis of lignocellulosic biomass was developed using Fourier transform near-infrared (FT-NIR) spectroscopic techniques. The new method is less time-consuming and expensive than traditional wet chemistry. A mathematical model correlated FT-NIR spectra with concentrations determined by wet chemistry. Chemical compositions of corn stover and switchgrass were evaluated in terms of glucose, xylose, galactose, arabinose, mannose, lignin, and ash. Model development evaluated multivariate regressions, spectral transform algorithms, and spectral pretreatments and selected partial least squares regression, log(1/R), and extended multiplicative signal correction, respectively. Chemical composition results indicated greater variability in corn stover than switchgrass, especially among botanic parts. Also, glucose percentage was higher in internodes (\u3e40%) than nodes or leaves (~30- 40%). Leaves had the highest percentage of lignin (~23-25%) and ash (~4-9%). Husk had the highest total sugar percentage (~77%). Individual FT-NIR predictive models were developed with good accuracy for corn stover and switchgrass. Root mean square errors for prediction (RMSEPs) from crossvalidation for glucose, xylose, galactose, arabinose, mannose, lignin and ash were 0.633, 0.620, 0.235, 0.374, 0.203, 0.458 and 0.266 (%w/w), respectively for switchgrass, and 1.407, 1.346, 0.201, 0.341, 0.321, 1.087 and 0.700 (%w/w), respectively for corn stover. A unique general model for corn stover and switchgrass was developed and validated for general biomass using a combination of independent samples of corn stover, switchgrass and wheat straw. RMSEPs of this general model using cross-validation were 1.153, 1.208, 0.425, 0.578, 0.282, 1.347 and 0.530 %w/w for glucose, xylose, galactose, arabinose, mannose, lignin and ash, respectively. RMSEPs for independent validation were less than those obtained by cross-validation. Prediction of major constituents satisfied standardized quality control criteria established by the American Association of Cereal Chemists. Also, FT-NIR analysis predicted higher heating value (HHV) with a RMSEP of 53.231 J/g and correlation of 0.971. An application of the developed method is the rapid analysis of the chemical composition of biomass feedstocks to enable improved targeting of plant botanic components to conversion processes including, but not limited to, fermentation and gasification

The SHARDDS survey: limits on planet occurrence rates based on point sources analysis via the Auto-RSM framework

In the past decade, HCI surveys provided new insights about the frequency and properties of substellar companions at separation larger than 5 au. In this context, our study aims to detect and characterise potential exoplanets and brown dwarfs within debris disks, by considering the SHARDDS survey, which gathers 55 Main Sequence stars with known bright debris disk. We rely on the AutoRSM framework to perform an in-depth analysis of the targets, via the computation of detection maps and contrast curves. A clustering approach is used to divide the set of targets in multiple subsets, in order to reduce the computation time by estimating a single optimal parametrisation for each considered subset. The use of Auto-RSM allows to reach high contrast at short separations, with a median contrast of 10-5 at 300 mas, for a completeness level of 95%. Detection maps generated with different approaches are used along with contrast curves, to identify potential planetary companions. A new planetary characterisation algorithm, based on the RSM framework, is developed and tested successfully, showing a higher astrometric and photometric precision for faint sources compared to standard approaches. Apart from the already known companion of HD206893 and two point-like sources around HD114082 which are most likely background stars, we did not detect any new companion around other stars. A correlation study between achievable contrasts and parameters characterising HCI sequences highlights the importance of the strehl, wind speed and wind driven halo to define the quality of high contrast images. Finally, planet detection and occurrence frequency maps are generated and show, for the SHARDDS survey, a high detection rate between 10 and 100 au for substellar companions with mass >10MJ

Passive Acoustic Emissions in a V-blender

The pharmaceutical manufacturing process consists of a number of batch steps; each step must be monitored and controlled to ensure quality standards are met. The development of process analytical technologies (PAT) can improve product monitoring with the aim of increasing efficiency, product quality and consistency and creating a better understanding of the manufacturing process. This work investigates the feasibility of using passive acoustic emissions (PAE) to monitor particulates in a V-blender. An accelerometer was attached to the lid of a V-blender to measure vibrations from the tumbling solids. A wavelet filter removed the oscillations in the signals from the motion of the shell, focusing on the emissions from the particle interactions. The particle size, fill level and scale affected the acoustic emissions through changes in the particle momentum. Changes in particle cohesiveness and flowability were also reflected in the measured emissions. Powder properties and behavior are critical to efficient and successful manufacturing of pharmaceutical tablets. As the powders must be transferred between the different manufacturing stages, the flowability of powders is critical. Trials were conducted to investigate the effect of moisture content of a powder on its flowability. Through avalanche behavior, it was found that the flowability and the dynamic density of a powder change with moisture content. PAEs were used to detect changes in solids moisture content as solids tumbled within the V-blender. It was found that particle mass, coefficient of restitution (COR) and flowability impacted the amplitude of the acoustic emissions. To further investigate the effects of particle flowability, PAEs were used to monitor lubricant addition. The amplitudes of the acoustic emissions were sensitive to the lubricant addition due to changes in the flowability. A trend in the emission amplitude allowed for the progression of the lubricant mixing to be followed. Overall, the research supports the feasibility of PAEs as a PAT for mixing in a tumbling blender to increase process knowledge and improve product quality

Assessing The Biophysical Naturalness Of Grassland In Eastern North Dakota With Hyperspectral Imagery

Over the past two decades, non-native species within grassland communities have quickly developed due to human migration and commerce. Invasive species like Smooth Brome grass (Bromus inermis) and Kentucky Blue Grass (Poa pratensis), seriously threaten conservation of native grasslands. This study aims to discriminate between native grasslands and planted hayfields and conservation areas dominated by introduced grasses using hyperspectral imagery. Hyperspectral imageries from the Hyperion sensor on EO-1 were acquired in late spring and late summer on 2009 and 2010. Field spectra for widely distributed species as well as smooth brome grass and Kentucky blue grass were collected from the study sites throughout the growing season. Imagery was processed with an unmixing algorithm to estimate fractional cover of green and dry vegetation and bare soil. As the spectrum is significantly different through growing season, spectral libraries for the most common species are then built for both the early growing season and late growing season. After testing multiple methods, the Adaptive Coherence Estimator (ACE) was used for spectral matching analysis between the imagery and spectral libraries. Due in part to spectral similarity among key species, the results of spectral matching analysis were not definitive. Additional indexes, Level of Dominance and Band variance , were calculated to measure the predominance of spectral signatures in any area. A Texture co-occurrence analysis was also performed on both Level of Dominance and Band variance indexes to extract spatial characteristics. The results suggest that compared with disturbed area, native prairie tend to have generally lower Level of Dominance and Band variance as well as lower spatial dissimilarity. A final decision tree model was created to predict presence of native or introduced grassland. The model was more effective for identification of Mixed Native Grassland than for grassland dominated by a single species. The discrimination of native and introduced grassland was limited by the similarity of spectral signatures between forb-dominated native grasslands and brome-grass stands. However, saline native grasslands were distinguishable from brome grass

Context dependent spectral unmixing.

A hyperspectral unmixing algorithm that finds multiple sets of endmembers is proposed. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel function that combines context identification and unmixing. This joint objective function models contexts as compact clusters and uses the linear mixing model as the basis for unmixing. Several variations of the CDSU, that provide additional desirable features, are also proposed. First, the Context Dependent Spectral unmixing using the Mahalanobis Distance (CDSUM) offers the advantage of identifying non-spherical clusters in the high dimensional spectral space. Second, the Cluster and Proportion Constrained Multi-Model Unmixing (CC-MMU and PC-MMU) algorithms use partial supervision information, in the form of cluster or proportion constraints, to guide the search process and narrow the space of possible solutions. The supervision information could be provided by an expert, generated by analyzing the consensus of multiple unmixing algorithms, or extracted from co-located data from a different sensor. Third, the Robust Context Dependent Spectral Unmixing (RCDSU) introduces possibilistic memberships into the objective function to reduce the effect of noise and outliers in the data. Finally, the Unsupervised Robust Context Dependent Spectral Unmixing (U-RCDSU) algorithm learns the optimal number of contexts in an unsupervised way. The performance of each algorithm is evaluated using synthetic and real data. We show that the proposed methods can identify meaningful and coherent contexts, and appropriate endmembers within each context. The second main contribution of this thesis is consensus unmixing. This approach exploits the diversity and similarity of the large number of existing unmixing algorithms to identify an accurate and consistent set of endmembers in the data. We run multiple unmixing algorithms using different parameters, and combine the resulting unmixing ensemble using consensus analysis. The extracted endmembers will be the ones that have a consensus among the multiple runs. The third main contribution consists of developing subpixel target detectors that rely on the proposed CDSU algorithms to adapt target detection algorithms to different contexts. A local detection statistic is computed for each context and then all scores are combined to yield a final detection score. The context dependent unmixing provides a better background description and limits target leakage, which are two essential properties for target detection algorithms

Gait recognition based on shape and motion analysis of silhouette contours

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods

Use of Fourier Transform Infrared Spectroscopy for the Classification and Identification of Bacteria of Importance to the Food Industry

The aim of this work was to use Fourier Transform Infrared Spectroscopy to characterize and identify bacteria of particular significance to the food industry. FT-IR spectroscopy is a rapid technique that can be applied to all groups of bacteria. The two objectives were to determine a suitable sampling procedure to record a spectrum and to determine a suitable statistical technique to identify characteristic regions of the spectrum associated with the genus and, potentially, the species. Pure cultures of bacteria were grown in broth, suspended in saline and dried to produce a film on a halide salt crystal. These films were then used to produce FT-IR spectra. In total, 80 spectra were recorded from seven genera, seven species and four strains of bacteria. Some of the spectra were considered to be too low in intensity to be included in statistical analysis. Data points from three specific windows of the remaining spectra were used to determine spectral distances between spectra. These spectral distances were used to perform cluster analysis using Ward\u27s method, the Complete Linkage method and the Centroid method. The statistical analysis created successful clusters for several of the species used but was inconclusive overall in being able to distinguish between spectra at the genus, species and strain level. This may be due to inconsistent growth of bacteria and insufficient manipulation of the data. This study has shown the potential for FT-IR spectroscopy to be used to identify bacteria with significance for food but further development is needed to reproduce the consistent results demonstrated in current literature