Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

The Associative Dimension of Issue Ownership

Issue ownership is commonly conceptualized as multidimensional, consisting of a "competence" dimension and an "associative" dimension. Because existing operationalizations of issue ownership tap only the former dimension, we focus on associative issue ownership: the spontaneous identification between specific issues and specific parties in the minds of voters. Survey evidence from Belgium shows that the associative dimension of issue ownership can be measured, that it differs from competence issue ownership, and that it is an independent determinant of voting behavior.

Impairment of mossy fiber long-term potentiation and associative learning in pituitary adenylate cyclase activating polypeptide type I receptor-deficient Mice

The pituitary adenylate cyclase activating polypeptide (PACAP) type I receptor (PAC1) is a G-protein-coupled receptor binding the strongly conserved neuropeptide PACAP with 1000-fold higher affinity than the related peptide vasoactive intestinal peptide. PAC1-mediated signaling has been implicated in neuronal differentiation and synaptic plasticity. To gain further insight into the biological significance of PAC1-mediated signaling in vivo, we generated two different mutant mouse strains, harboring either a complete or a forebrain-specific inactivation of PAC1. Mutants from both strains show a deficit in contextual fear conditioning, a hippocampus-dependent associative learning paradigm. In sharp contrast, amygdala-dependent cued fear conditioning remains intact. Interestingly, no deficits in other hippocampus-dependent tasks modeling declarative learning such as the Morris water maze or the social transmission of food preference are observed. At the cellular level, the deficit in hippocampus-dependent associative learning is accompanied by an impairment of mossy fiber long-term potentiation (LTP). Because the hippocampal expression of PAC1 is restricted to mossy fiber terminals, we conclude that presynaptic PAC1-mediated signaling at the mossy fiber synapse is involved in both LTP and hippocampus-dependent associative learning.

Indirect hydrogen monitoring by chemi-ionisation following an associative ionisation pathway after metastable helium atoms production by electron impact ionisation: Protonation and deuteration of carrier gas

Gas chromatography (GC) is an analytical tool very useful to investigate the composition of gaseous mixtures. However, hydrogen (H2) detection after a GC separation is only possible with a Thermal Conductivity Detector (TCD), a Helium Ionisation Detector (HID) or expensive Atomic Emission Detector (AED). Recently, indirect H2 detection by GC coupled to mass spectrometry (MS) was demonstrated but the mechanism of carrier gas protonation remained unclear. With electron impact as ionisation source of MS and helium (He) as GC carrier gas, H2 is not ionised according the expected Penning ionisation neither according to the Associative ionisation. Rearrangement ionisation (RI) was found to be the main channel for H2 and D2 ionisation under GC-MS conditions used in most of laboratories using GC-MS, leading to the formation of [He−H]+ and [He−D]+ ions.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

Associative Issue Ownership as a Determinant of Voters' Campaign Attention

Campaigns raise public interest in politics and allow parties to convey their messages to voters. However, voters' exposure and attention during campaigns are biased towards parties and candidates they like. This hinders parties' ability to reach new voters. This paper theorises and empirically tests a simple way in which parties can break partisan selective attention: owning an issue. When parties own issues that are important for a voter, that voter is more likely to notice them. Using survey data collected prior to the 2009 Belgian regional elections it is shown that this effect exists independent of partisan preferences and while controlling for the absolute visibility of a party in the media. This indicates that issue ownership has an independent impact on voters' attention to campaigns. This finding shows that owning salient issues yields (potential) advantages for parties, since getting noticed is a prerequisite for conveying electoral messages and increasing electoral success.

'Steal me if you can!' The impact of campaign messages on associative issue ownership

Whereas extant work on issue ownership treats voters' issue ownership perceptions as independent variables to explain electoral choice or party behaviour, this article examines whether parties can, by communicating on an issue, turn voters' perceptions of issue ownership to their advantage. In contrast to most previous studies that have focused on competence ownership - measured as a party's capacity to handle an issue - this article analyses the short-term and long-term impact of campaign messages on voters' perceptions of associative ownership, which refers to the voters' spontaneous party-issue association, regardless of whether or not voters consider the party as the most competent at dealing with the issue at hand. Based on an online experimental design in Belgium, we show that parties are unable to steal issues that are associated with another party. However, by communicating on their own issues, parties can reinforce their reputation as an associative owner - but only in the short run and only if their previous ownership reputation is not overly strong.

Effects of Enriched Physical and Social Environments on Motor Performance, Associative Learning, and Hippocampal Neurogenesis in Mice.

We have studied the motor abilities and associative learning capabilities of adult mice placed in different enriched environments. Three-month-old animals were maintained for a month alone (AL), alone in a physically enriched environment (PHY), and, finally, in groups in the absence (SO) or presence (SOPHY) of an enriched environment. The animals' capabilities were subsequently checked in the rotarod test, and for classical and instrumental learning. The PHY and SOPHY groups presented better performances in the rotarod test and in the acquisition of the instrumental learning task. In contrast, no significant differences between groups were observed for classical eyeblink conditioning. The four groups presented similar increases in the strength of field EPSPs (fEPSPs) evoked at the hippocampal CA3-CA1 synapse across classical conditioning sessions, with no significant differences between groups. These trained animals were pulse-injected with bromodeoxyuridine (BrdU) to determine hippocampal neurogenesis. No significant differences were found in the number of NeuN/BrdU double-labeled neurons. We repeated the same BrdU study in one-month-old mice raised for an additional month in the above-mentioned four different environments. These animals were not submitted to rotarod or conditioned tests. Non-trained PHY and SOPHY groups presented more neurogenesis than the other two groups. Thus, neurogenesis seems to be related to physical enrichment at early ages, but not to learning acquisition in adult mice.

Feedback-related Brain Potential Activity Complies with Basic Assumptions of Associative Learning Theory

Feedback-related negativity (FRN) is an ERP component that distinguishes positive from negative feedback. FRN has been hypothesized to be the product of an error signal that may be used to adjust future behavior. In addition, associative learning models assume that the trial-to-trial learning of cueoutcome mappings involves the minimization of an error term. This study evaluated whether FRN is a possible electrophysiological correlate of this error term in a predictive learning task where human subjects were asked to learn different cueoutcome relationships. Specifically, we evaluated the sensitivity of the FRN to the course of learning when different stimuli interact or compete to become a predictor of certain outcomes. Importantly, some of these cues were blocked by more informative or predictive cues (i.e., the blocking effect). Interestingly, the present results show that both learning and blocking affect the amplitude of the FRN component. Furthermore, independent analyses of positive and negative feedback event-related signals showed that the learning effect was restricted to the ERP component elicited by positive feedback. The blocking test showed differences in the FRN magnitude between a predictive and a blocked cue. Overall, the present results show that ERPs that are related to feedback processing correspond to the main predictions of associative learning models. ■

The time course of ethanol tolerance: associative learning

The effect of different contextual stimuli on different ethanol-induced internal states was investigated during the time course of both the hypothermic effect of the drug and of drug tolerance. Minimitters were surgically implanted in 16 Wistar rats to assess changes in their body temperature under the effect of ethanol. Rat groups were submitted to ethanol or saline trials every other day. The animals were divided into two groups, one receiving a constant dose (CD) of ethanol injected intraperitoneally, and the other receiving increasing doses (ID) during the 10 training sessions. During the ethanol training sessions, conditioned stimuli A (tone) and B (buzzer) were presented at "state +" (35 min after drug injection) and "state -" (170 min after drug injection), respectively. Conditioned stimuli C (bip) and D (white noise) were presented at moments equivalent to stimuli A and B, respectively, but during the saline training sessions. All stimuli lasted 15 min. The CD group, but not the ID group, developed tolerance to the hypothermic effect of ethanol. Stimulus A (associated with drug "state +") induced hyperthermia with saline injection in the ID group. Stimulus B (associated with drug "state -") reduced ethanol tolerance in the CD group and modulated the hypothermic effect of the drug in the ID group. These results indicate that contextual stimuli acquire modulatory conditioned properties that are associated with the time course of both the action of the drug and the development of drug tolerance.