TaLAM: Mapping Land Cover in Lowlands and Uplands with Satellite Imagery

End-of-Project ReportThe Towards Land Cover Accounting and Monitoring (TaLAM) project is part of Ireland’s response to creating a national land cover mapping programme. Its aims are to demonstrate how the new digital map of Ireland, Prime2, from Ordnance Survey Ireland (OSI), can be combined with satellite imagery to produce land cover maps

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

Object-based Analysis and Multispectral Low-altitude Remote Sensing for Low-cost Mapping of Chalk Stream Macrophytes

Their small size and high biodiversity have until now made UK chalk streams unsuitable subjects for study with remote sensing techniques. Future technological developments are however likely to change this. The study described in this paper shows how high resolution multispectral images taken with an off-the-shelve, infrared sensitive digital camera, can give a first insight into future opportunities for mapping and monitoring of submerged chalk stream environments. The high resolution multispectral images have been used in combination with Object Based Image Analysis (OBIA) techniques to map submerged vegetation. Preliminary results show that the Near Infrared Red information recorded by the camera greatly improves the classification of individual macrophyte species. The benefit of the object-based image analysis approach is at the presented stage only limited, but a first attempt at creating a robust rule set has been applied to photos taken at two different field sites with some success. The analysis also showed how texture features are useful for the separability between macrophyte classes. Overall the results are promising for further applications of remote sensing techniques to chalk streams as well as for application of the low cost sensor set-up

Olivine or Impact Melt: Nature of the "Orange" Material on Vesta from Dawn

NASA's Dawn mission observed a great variety of colored terrains on asteroid (4) Vesta during its survey with the Framing Camera (FC). Here we present a detailed study of the orange material on Vesta, which was first observed in color ratio images obtained by the FC and presents a red spectral slope. The orange material deposits can be classified into three types, a) diffuse ejecta deposited by recent medium-size impact craters (such as Oppia), b) lobate patches with well-defined edges, and c) ejecta rays from fresh-looking impact craters. The location of the orange diffuse ejecta from Oppia corresponds to the olivine spot nicknamed "Leslie feature" first identified by Gaffey (1997) from ground-based spectral observations. The distribution of the orange material in the FC mosaic is concentrated on the equatorial region and almost exclusively outside the Rheasilvia basin. Our in-depth analysis of the composition of this material uses complementary observations from FC, the visible and infrared spectrometer (VIR), and the Gamma Ray and Neutron Detector (GRaND). Combining the interpretations from the topography, geomorphology, color and spectral parameters, and elemental abundances, the most probable analog for the orange material on Vesta is impact melt

Delivery of Dark Material to Vesta via Carbonaceous Chondritic Impacts

NASA's Dawn spacecraft observations of asteroid (4) Vesta reveal a surface with the highest albedo and color variation of any asteroid we have observed so far. Terrains rich in low albedo dark material (DM) have been identified using Dawn Framing Camera (FC) 0.75 {\mu}m filter images in several geologic settings: associated with impact craters (in the ejecta blanket material and/or on the crater walls and rims); as flow-like deposits or rays commonly associated with topographic highs; and as dark spots (likely secondary impacts) nearby impact craters. This DM could be a relic of ancient volcanic activity or exogenic in origin. We report that the majority of the spectra of DM are similar to carbonaceous chondrite meteorites mixed with materials indigenous to Vesta. Using high-resolution seven color images we compared DM color properties (albedo, band depth) with laboratory measurements of possible analog materials. Band depth and albedo of DM are identical to those of carbonaceous chondrite xenolith-rich howardite Mt. Pratt (PRA) 04401. Laboratory mixtures of Murchison CM2 carbonaceous chondrite and basaltic eucrite Millbillillie also show band depth and albedo affinity to DM. Modeling of carbonaceous chondrite abundance in DM (1-6 vol%) is consistent with howardite meteorites. We find no evidence for large-scale volcanism (exposed dikes/pyroclastic falls) as the source of DM. Our modeling efforts using impact crater scaling laws and numerical models of ejecta reaccretion suggest the delivery and emplacement of this DM on Vesta during the formation of the ~400 km Veneneia basin by a low-velocity (<2 km/sec) carbonaceous impactor. This discovery is important because it strengthens the long-held idea that primitive bodies are the source of carbon and probably volatiles in the early Solar System.Comment: Icarus (Accepted) Pages: 58 Figures: 15 Tables:

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF