Automatic Extraction of Planetary Image Features

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration

Unsupervised Detection of Planetary Craters by a Marked Point Process

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features

DRBM-ClustNet: A Deep Restricted Boltzmann-Kohonen Architecture for Data Clustering

A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed. This core-clustering engine consists of a Deep Restricted Boltzmann Machine (DRBM) for processing unlabeled data by creating new features that are uncorrelated and have large variance with each other. Next, the number of clusters are predicted using the Bayesian Information Criterion (BIC), followed by a Kohonen Network-based clustering layer. The processing of unlabeled data is done in three stages for efficient clustering of the non-linearly separable datasets. In the first stage, DRBM performs non-linear feature extraction by capturing the highly complex data representation by projecting the feature vectors of $d$ dimensions into $n$ dimensions. Most clustering algorithms require the number of clusters to be decided a priori, hence here to automate the number of clusters in the second stage we use BIC. In the third stage, the number of clusters derived from BIC forms the input for the Kohonen network, which performs clustering of the feature-extracted data obtained from the DRBM. This method overcomes the general disadvantages of clustering algorithms like the prior specification of the number of clusters, convergence to local optima and poor clustering accuracy on non-linear datasets. In this research we use two synthetic datasets, fifteen benchmark datasets from the UCI Machine Learning repository, and four image datasets to analyze the DRBM-ClustNet. The proposed framework is evaluated based on clustering accuracy and ranked against other state-of-the-art clustering methods. The obtained results demonstrate that the DRBM-ClustNet outperforms state-of-the-art clustering algorithms.Comment: 14 pages, 7 figure

HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas

HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas

Determining Context Factors for Hybrid Development Methods with Trained Models

Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiarities of context factors influencing method and practice selection. In this paper, we utilize exploratory factor analysis and logistic regression analysis to learn such context factors and to identify methods that are correlated with these factors. Our analysis is based on 829 data points from the HELENA dataset. We provide five base clusters of methods consisting of up to 10 methods that lay the foundation for devising hybrid development methods. The analysis of the five clusters using trained models reveals only a few context factors, e.g., project/product size and target application domain, that seem to significantly influence the selection of methods. An extended descriptive analysis of these practices in the context of the identified method clusters also suggests a consolidation of the relevant practice sets used in specific project contexts

Retinopathy in old persons with and without diabetes mellitus: the Age, Gene/Environment Susceptibility--Reykjavik Study (AGES-R).

To access full text version of this article. Please click on the hyperlink "View/open" at the bottom of this pageWe aimed to describe the prevalence of retinopathy in an aged cohort of Icelanders with and without diabetes mellitus. The study population consisted of 4,994 persons aged ≥ 67 years, who participated in the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-R). Type 2 diabetes mellitus was defined as HbA(1c) ≥ 6.5% (>48 mmol/mol). Retinopathy was assessed by grading fundus photographs using the modified Airlie House adaptation of the Early Treatment Diabetic Retinopathy Study protocol. Associations between retinopathy and risk factors were estimated using odds ratios obtained from multivariate analyses. The overall prevalence of retinopathy in AGES-R was 12.4%. Diabetes mellitus was present in 516 persons (10.3%), for 512 of whom gradable fundus photos were available, including 138 persons (27.0%, 95% CI 23.2, 31.0) with any retinopathy. Five persons (1.0%, 95% CI 0.3, 2.3) had proliferative retinopathy. Clinically significant macular oedema was present in five persons (1.0%, 95% CI 0.3, 2.3). Independent risk factors for retinopathy in diabetic patients in a multivariate model included HbA(1c), insulin use and use of oral hypoglycaemic agents, the last two being indicators of longer disease duration. In 4478 participants without diabetes mellitus, gradable fundus photos were available for 4,453 participants, with retinopathy present in 476 (10.7%, 95% CI 9.8, 11.6) and clinically significant macular oedema in three persons. Independent risk factors included increasing age and microalbuminuria. Over three-quarters (78%) of retinopathy cases were found in persons without diabetes and a strong association between microalbuminuria and non-diabetic retinopathy was found. These results may have implications for patient management of the aged.NIH N01-AG-12100 NIH/NIA, National Eye Institute (NEI) of the NIH ZIAEY000401, Hjartavernd (the Icelandic Heart Association), Althingi (the Icelandic Parliament), University of Iceland

Spatial and temporal variation in foraging of breeding red‐throated divers

Differing environmental conditions can have profound effects on many behaviours in animals, especially where species have large geographic ranges. Seasonal changes or progression through life history stages impose differential constraints, leading to changes in behaviours. Furthermore, species which show flexibility in behaviours, may have a higher capacity to adapt to anthropogenic-induced changes to their environment. The red-throated diver (RTD) is an aquatic bird, that is able to forage in both freshwater and marine environments, though little else is known about its behaviours and its capacity to adapt to different environmental conditions. Here, we use time-depth recorders and saltwater immersion loggers to examine the foraging behaviour of RTDs from three regions across northwest Europe. We found that in the breeding season, birds from two regions (Iceland and Scotland) foraged in the marine environment, while birds from Finland, foraged predominantly in freshwater. Most of the differences in diving characteristics were at least partly explained by differences in foraging habitat. Additionally, while time spent foraging did not change through the breeding season, dives generally became more pelagic and less benthic over the season, suggesting RTDs either switched prey or followed vertical prey movements, rather than increasing foraging effort. There was a preference for foraging in daylight over crepuscular hours, with a stronger effect at two of the three sites. Overall, we provide the first investigation of RTD foraging and diving behaviour from multiple geographic regions and demonstrate variation in foraging strategies in this generalist aquatic predator, most likely due to differences in their local environment.Peer reviewe