Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Inheritance of Resistance to Rice Tungro Spherical Virus in a Near-Isogenic Line Derived from Utri Merah and in Rice Cultivar TKM6

Resistance to rice virus diseases is an important requirement in many Southeast Asian rice breeding programs. Inheritance of resistance to rice tungro spherical virus (RTSV) in TW5, a near-isogenic line derived from Indonesian rice cultivar Utri Merah, was compared to that in TKM6, an Indian rice cultivar. Both TKM6 and Utri Merah are cultivars resistant to RTSV infections. Crosses were made between TKM6 and TN1, a susceptible cultivar, and between TW5 and TN1, and F3 lines were evaluated for their resistance to RTSV using two RTSV inoculum sources and a serological assay (ELISA). In TKM6, the resistance to the mixture of RTSV-V + RTBV inoculum source was controlled by a single recessive gene, whereas in TW5, the resistance was controlled by two recessive genes. A single recessive gene, however, controlled the resistance in TW5 when another RTSV variant, RTSV-VI, was used, suggesting that the resistance in TW5 depends on the nature of the RTSV inoculum used. RT-PCR, sequence, and phylogenetic analyses confirmed that RTSV-VI inoculum differs from RTSV-V inoculum and accurate phenotyping of the resistance to RTSV requires the use of a genetic marker.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.

Design of compact coupled-line couplers

The size of rat-race and branch-line couplers can be reduced by using periodic loading or artificial transmission lines. The objective of this work is to extend the idea of size reduction through periodic loading to coupled-line 90° hybrids. A procedure for the extraction of the characteristic parameters of a coupled-line 4-port from a single set of S-parameters is described. This method can be employed to design of coupled artificial transmission line couplers of arbitrary geometry. The procedure is illustrated through the design a broadside-coupled stripline hybrid, periodically loaded with stubs. Measured results for a prototype coupler confirm the validity of the theory.

Occurrence of LINE, gypsy-like, and copia-like retrotransposons in the clonally propagated sweet potato (Ipomoea batatas L.)

Retrotransposons are a class of transposable elements that represent a major fraction of the repetitive DNA of most eukaryotes. Their abundance stems from their expansive replication strategies. We screened and isolated sequence fragments of long terminal repeat (LTR), gypsy-like reverse transcriptase (rt) and gypsy-like envelope (env) domains, and two partial sequences of non-LTR retrotransposons, long interspersed element (LINE), in the clonally propagated allohexaploid sweet potato (Ipomoea batatas (L.) Lam.) genome. Using dot-blot hybridization, these elements were found to be present in the ~1597 Mb haploid sweet potato genome with copy numbers ranging from ~50 to ~4100 as observed in the partial LTR (IbLtr-1) and LINE (IbLi-1) sequences, respectively. The continuous clonal propagation of sweet potato may have contributed to such a multitude of copies of some of these genomic elements. Interestingly, the isolated gypsy-like env and gypsy-like rt sequence fragments, IbGy-1 (~2100 copies) and IbGy-2 (~540 copies), respectively, were found to be homologous to the Bagy-2 cDNA sequences of barley (Hordeum vulgare L.). Although the isolated partial sequences were found to be homologous to other transcriptionally active elements, future studies are required to determine whether they represent elements that are transcriptionally active under normal and (or) stressful conditions.

Mission unreachable : how Jaman is shaping the future of on-line distribution

Perhaps the most innovative of all independent OLD ventures specialising in ROW content is Jaman. Founded in 2007 by IT entrepreneur Gaurav Dhillon, and based in San Mateo, California, Jaman is a quality specialist distributor of non-Hollywood films. As of late 2010, Jaman had 1.8 million registered users and attracts viewers from most countries in the world. 75% of all use is generated from outside the U.S. Jaman does very well in English speaking parts of the world, particularly current and former Commonwealth countries. The United Kingdom accounts for 29% of users, North America (U.S. and Canada) 26%, and India represents 23%. Jaman is sometimes referred to as ‘social cinema’: a website which brings together the critique and review of a cinephile website (the forums of Rue-morgue.com for cinefantastique movie fans for example) with the social interaction, community and functionality of a social media site (for example Facebook.com). Jaman could be considered a pioneer in this space; a first mover in wrapping commercial movie downloading in an interactive social experience.

Postcode segmentation and recognition using projections and bispectral features

A system to segment and recognize Australian 4-digit postcodes from address labels on parcels is described. Images of address labels are preprocessed and adaptively thresholded to reduce noise. Projections are used to segment the line and then the characters comprising the postcode. Individual digits are recognized using bispectral features extracted from their parallel beam projections. These features are insensitive to translation, scaling and rotation, and robust to noise. Results on scanned images are presented. The system is currently being improved and implemented to work on-line.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.