Variation and directional anisotropy of reflectance at the crown scale — Implications for tree species classification in digital aerial images

Tree species classification is still solved at insufficient reliability in airborne optical data. The variation caused by directional reflectance anisotropy hampers image-based solutions. In addition, trees show considerable within-species variation in reflectance properties. We examined these phenomena at the single-tree level, using the Leica ADS40 line sensor and XPro software, which constitute the first photogrammetric large-format multispectral system to provide target reflectance images. To analyze the influence of illumination conditions in the canopy, we developed a method in which the crown shape as well as between-tree occlusions and shading were modeled, using dense LiDAR data. The precision of the ADS40 reflectance images in well-defined surfaces was 5% as coefficient of variation when 1−4-km image data were fused. The range of reflectance anisotropy was ± 30% for trees near the solar principal plane, with differences between bands and species. Because of the anisotropy differences observed, the spectral separability of the tree species in different bands is dependent on the view-illumination geometry. The within-species variation was high; the coefficient of variation was 13−31%. The contribution of tree and stand variables to anisotropy-normalized reflectance variation was examined. The effects of the species composition of adjacent trees were substantial in NIR and this variation hampers spectral classification in mixed stands. We also studied species- and band-specific intracrown brightness patterns, and we suggest their use as high-order image features in species classification. A species classification accuracy of up to 80% was obtained using 4-km data, which showed the high potential of the ADS40.     

Jitter characteristics of an on-chip voltage reference-locked time-to-digital converter

The noise and jitter characteristics of an on-chip voltage reference-locked ring oscillator used in the time-to-digital converter (TDC) of the integrated receiver of a pulsed time-of-flight laser rangefinder are presented. The frequency of the ring oscillator, 683 MHz, was locked to the on-chip voltage reference by means of a frequency-to-voltage converter, resulting in better than 90 ppm/°C stability. The noise and jitter transfer characteristics of the loop were derived, and simulations were performed to see the effects of different noise types (white and 1/f noise) on the cumulative jitter of the locked ring oscillator. Finally, these results were verified by jitter measurements performed using an integrated time-to-digital converter (TDC) fabricated on the same die (0.18 μm CMOS process). The cumulative jitter of the on-chip reference-locked ring oscillator was less than 30 ps (sigma value) over a time range of 70 ns, which made it possible to use this oscillator as the heart of a TDC when aiming at centimetre-level precision (1 cm = 67 ps) in laser ranging.     

Improvement of the Conversion Performance of a Resonating Multimode Microelectromechanical Mixer-Filter Through Parametric Amplification

The down-conversion performance of a multimodal microelectromechanical mixer-filter is improved over 30 dB by parametric amplification. The input signal is an amplitude-modulated signal with a carrier frequency of 0.5 GHz. The obtained amplification is shown to depend on the particular eigenmode in a predetermined way. Finally, a qualitative explanation for the behavior is given.     

Greenhouse impact assessment of peat-based Fischer–Tropsch diesel life-cycle

New raw materials for transportation fuels need to be introduced, in order to fight against climate change and also to cope with increasing risks of availability and price of oil. Peat has been recognised suitable raw material option for diesel produced by gasification and Fischer–Tropsch (FT) synthesis. The energy content of Finnish peat reserves is remarkable. In this study, the greenhouse impact of peat-based FT diesel production and utilisation in Finland was assessed from the life-cycle point of view. In 100 year's time horizon the greenhouse impact of peat-based FT diesel is likely larger than the impact of fossil diesel. The impact can somewhat be lowered by producing peat from the agricultural peatland (strong greenhouse gas emissions from the decaying peatlayer are avoided) with new peat production technique, and utilising the produced biomass from the after-treatment area for diesel also. If diesel production is integrated with pulp and paper mill to achieve energy efficiency benefits and if the electricity demand can be covered by zero emission electricity, the greenhouse impact of peat-based FT diesel reduces to the level of fossil diesel when agricultural peatland is used, and is somewhat higher when forestry-drained peatland is used as raw material source.     

Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics

Forest inventories based on single-tree interpretation of airborne laser scanning (ALS) data often rely on an allometric estimation chain in which inaccuracies in the estimates of the diameter at breast height (DBH) propagate to other characteristics of interest such as the stem volume. Our purpose was to test nearest neighbor imputation by the k-Most Similar Neighbor (k-MSN) and the Random Forest (RF) methods for the simultaneous estimation of species, DBH, height and stem volume using ALS data. The predictors included computational alpha shape metrics and variables based on the height and intensity distributions in the ALS data. Separate data sets covering 1898 and 1249 dominant to intermediate trees in a typical Scandinavian stand structure were used for training and validation, respectively. RF proved to be a flexible method with an ability to handle 1846 predictors with no need for their reduction. Classification of Scots pine, Norway spruce and deciduous trees showed an accuracy of 78%, and the estimates of DBH, height and volume had root mean square errors of 13%, 3%, and 31%, respectively, when evaluated against the validation data. The two selection strategies implemented here reduced the number of candidate variables effectively without any substantial effect on the accuracy relative to the use of all predictors. Differences in k-MSN and RF imputations were marginal when the reduced sets of variables were used. Estimation accuracies could be maintained practically unchanged with only 12.5% of the initial reference data (237 trees), provided the distribution of the observations was similar in the reference and target data. Since we used information collected in the field for extracting the ALS point clouds for individual trees, our results represent an optimal case and should nevertheless be validated against automated tree delineation.     

BMC via on-the-fly determinization

This paper develops novel bounded model checking (BMC) techniques for asynchronous parallel systems. The aim is to increase the efficiency of BMC by exploiting the inherent concurrency in such systems. This added efficiency is gained by covering more reachable states within a given bound using two techniques. Firstly, a nonstandard execution model, step executions, where multiple actions can take place simultaneously is applied. Secondly, the number of executions the system can have is reduced by modeling the execution of the system components as if they were determinized. This determinization technique also enables the removal of the internal transitions of the components. Step executions can be further restricted to a subclass called process executions without losing any reachable states.The paper presents a translation scheme for BMC of reachability properties. The translation is from an asynchronous system where the components are modeled as labeled transition systems (LTSs) to a propositional formula. The models of the formula correspond to the step executions of the original system where each component is replaced with its determinized counterpart. The formula for step executions can be easily extended in such a way that its models correspond to the process executions of the system. The translation scheme has been implemented and some experimental comparisons performed. The results show that the bound needed to detect a violation of a reachability property is, for step and process executions, in most cases lower than in interleaving executions and that the running time of the model checker using process executions is smaller than of that using steps. Moreover, the performance compares favorably to a state-of-the-art interleaving BMC implementation in the NuSMV system.     

Flexible view recognition for indoor navigation based on Gabor filters and support vector machines

Real-world scenes are hard to segment into (relevant) objects and (irrelevant) background. In this paper, we argue for view-based vision, which does not use segmentation, and demonstrate a practical approach for recognizing textured objects and scenes in office environments.

A small set of Gabor filters is used to preprocess texture combinations from input images. The impulse responses of the filters are transformed into feature vectors that are fed to support vector machines. Pairwise feature comparisons decide the classification of views.

We validate the approach on a robot platform using three different types of target objects and indoor scenes: people, doorways, and written signs. The general-purpose system can run in real time, and that recognition accuracies of practical utility are obtained.     

Classification consistency analysis for bootstrapping gene selection

Consistency modelling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of operations such as classification, clustering, or gene selection on a training set is often found to be very different from the same operations on a testing set, presenting a serious consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, this paper proposes a new concept of classification consistency and applies it for microarray gene selection problem using a bootstrapping approach, with encouraging results.