Network-based correlated correspondence for unsupervised domain adaptation of hyperspectral satellite images

International audienceAdapting a model to changes in the data distribution is a relevant problem in machine learning and pattern recognition since such changes degrade the performances of classifiers trained on undistorted samples. This paper tackles the problem of domain adaptation in the context of hyperspectral satellite image analysis. We propose a new correlated correspondence algorithm based on network analysis. The algorithm finds a matching between two distributions, which preserves the geometrical and topological information of the corresponding graphs. We evaluate the performance of the algorithm on a shadow compensation problem in hyperspectral image analysis: the land use classification obtained with the compensated data is improve

What drives the kinetics and doping level in the electrochemical reactions of PEDOT:PSS?

The electrochemical dedoping and redoping processes of a thin poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) film immersed in an electrolyte are studied at different temperatures with time-resolved spectroelectrochemistry in the visible and near-infrared range. The spectral signatures of neutral, polaronic, and bipolaronic states of PEDOT are resolved using multivariate curve resolution analysis. Kinetic modeling of their dynamics reveals that both the dedoping and redoping are sequential processes and occur within a few hundred milliseconds in the system. Evaluation of the temperature-dependence with the Van't Hoff, Arrhenius, and Eyring formalisms highlights the role of entropy in both the establishment of the redox equilibrium at a given voltage bias and the reaction rates. This study provides a significant understanding of the fundamental mechanisms determining the level and rate of the electrochemical processes in PEDOT:PSS and will help tailor the design of faster and more efficient bioelectronic devices based on mixed ionic–electronic conductors

Augmenting a convolutional neural network with local histograms ::a case study in crop classification from high-resolution UAV imagery

The advent of affordable drones capable of taking high resolution images of agricultural fields creates new challenges and opportunities in aerial scene understanding. This paper tackles the problem of recognizing crop types from aerial imagery and proposes a new hybrid neural network architecture which combines histograms and convolutional units. We evaluate the performance of the proposed model on a 23-class classification task and compare it to other models. The result is an improvement of the classification performance

Reducing user intervention in incremental activityrecognition for assistive technologies

Activity recognition has recently gained a lot of interest and there already exist several methods to detect human activites based on wearable sensors. Most of the existing methods rely on a database of labelled activities that is used to train an offline activity recognition system. This paper presents an approach to build an online activity recognition system that do not require any a priori labelled data. The system incrementally learns activities by actively querying the user for labels. To choose when the user should be queried, we compare a method based on random sampling and another that uses a Growing Neural Gas (GNG). The use of GNG helps reducing the number of user queries by 20% to 30%

Indoor activity recognition by combining one-vs.-all neural network classifiers exploiting wearable and depth sensors

Activity recognition has recently gained a lot of interest and appears to be a promising approach to help the elderly population pursue an independent living. There already exist several methods to detect human activities based either on wearable sensors or on cameras but few of them combine the two modalities. This paper presents a strategy to enhance the robustness of indoor human activity recognition by combining wearable and depth sensors. To exploit the data captured by those sensors, we used an ensemble of binary one-vs-all neural network classifiers. Each activity-specific model was configured to maximize its performance. The performance of the complete system is comparable to lazy learning methods (k-NN) that require the whole dataset

Large-scale atmospheric circulation driving extreme climate events in the Mediterranean and related impacts (chapitre 6)

International audienc

6 - Large-scale atmospheric circulation driving extreme climate events in the Mediterranean and its related impacts

International audienc