Some historical musical instruments are still played today, and are prone to be affected by mechanical wear of superficial varnishes in direct contact with the musicians. In a previous work, an ad-hoc monitoring plan for historical violins, that involves the use multiple non-invasive analytic techniques, achieved good performance. However, the high number of techniques is a limitation if we want to perform frequent checks. In this work, our aim is to rely only on UV induced fluorescence images for performing a fast, preliminary check and then, if a possible alteration is detected, conduct spectroscopic analyses, which are more precise but also more time consuming. In this study, we explore the a-contrario framework in order to allow for the automatic detection of significant changes in the superficial varnishes. The difficulty of detecting the changes is represented by the need to define the significance of a change, in the absence of a ground truth provided by the expert. Tests performed on samples that simulate the effect of surface alteration during time show promising results.
KEYWORDS: Imaging systems, Data processing, Calibration, Data fusion, Sensors, Data modeling, Principal component analysis, Superposition, Reliability, Optical filters
Our application deals with waste sorting using an automatic system involving a hyperspectral camera. This latter provides the data for classification of the different kinds of waste allowing the evaluation of mechanical pre-sorting and its refinement. Hyperspectral data are processed using Support Vector Machine (SVM) binary classifiers that we propose to combine in the belief function theory (BFT) framework to take into account not only the performance of each binary classifier, but also its imprecision related for instance to the number of samples during the learning step. Having underlined the interest of BFT framework to deal with sparse classifiers, we study the performance of different combinations of classifiers.
In this work, we evaluate the relevance of current state of the art algorithms widely employed in the detection of cracks, for the specific context of aerial inspection, which is characterized by image quality degradation. In this study we focus on minimal cost path and on Marked Point Process algorithms, and we test their resilience to motion blur. The results show that the current strategies for defect detection are sensitive to the quality of input images; alternatively, we suggest some improvements based on a-contrario methods that are able to cope with significant motion blur.
In this study, we propose an automatic approach for detecting clouds, cloud shadows and mist present on optical remote
sensing images such as SPOT/HRVIR ones. This detection is necessary to not take their signal into account for land
studies from remote sensing data, such as land cover / land use classification, vegetation and soil moisture monitoring.
The adopted approach is based on Markov Random Field (MRF) modeling at two levels: pixel and object. The algorithm
is parameterized by six parameters that are rather robust since their value was kept identical for the processing of 39
SPOT/HRVIR images that corresponds to various acquisition conditions, seasons, and landscapes. Our method makes
use of three main cloud/shadow features:
- Clouds (or shadows) can be viewed as connex objects;
- Each cloud generates a shadow with similar shape and area;
- The direction of the relative position of a cloud and its shadow in the image is determined by acquisition conditions.
The first feature is modeled using a MRF on the pixel graph, and we show that the proposed model leads to the use of
hysteresis threshold techniques or growing region as far as local optimization is concerned. The two last features are
modeled using a MRF on the graph of cloud and shadow objects (detected from the previous step at pixel level), and we
show that the proposed model corresponds the mutual validation of cloud and shadow detections.
The hydrology of the Sahel is characterised by the degradation of the drainage network that induces a lack of large watersheds. In the Niamey degree, different studies have shown the importance of pools in the hydrology of the region. It was shown that different processes such as evaporation or deep infiltration depend on the level of filling of the pools. During the last years, several observations have shown different evolutions of these pools in the Niamey degree.
Our objectives in this paper are to identify the pools and their evolution. Our approach is based on high resolution optical remote sensing data, SPOT/HRV (20m) and SPOT5 (10m) images. This study uses a large data base of optical images (5 images in 1992, 1 image in 1994, 1 image in 1996 and 2 images in 2003). The identification approach is based on the NDVI coefficient calculated from Near Infrared and Red channels for each SPOT image. It is observed that the pools present the lowest values of NDVI in the studied optical images. The distribution of NDVI for pools is estimated for the different images, then a threshold is chosen to separate pools from the other types of land use.
First, we observe the evolution of pool surface and their number in the monsoon period from June to November in 1992. It is clearly shown that the maximum of pool surface corresponds to August 1992. This result is well correlated with rainfall statistics. Second, the estimation of pool surface and number from 1992 to 2003 shows an increase of the pools, particularly in the tiger bush. This behaviour could be explained particularly by the increase of the surface runoff in the region.
In global classifications using Markov Random Field (MRF) modelling, the neighbourhood form is generally considered as independent of its location in the image. Such an approach may lead to classification errors for pixels located at the segment borders. The solution proposed here consists in relaxing the assumption of fixed-form neighbourhood. However this non-stationary neighbourhood modelling is useful only if an efficient heuristic can be defined to perform the optimization. Ant colony optimization (ACO) is currently a popular algorithm. It models upon the behavior of social insects for computing strategies: the information gathered by simple autonomous mobile agents, called ants, is shared and exploited for problem solving. Here we propose to use the ACO and to exploit its ability of self-organization. The ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favouring paths within a same image segment. We show that this corresponds to an automatic adaptation of the neighbourhood to the segment form. Performance of this new approach is illustrated on a simulated image and on actual remote sensing images, SPOT4/HRV, representing agricultural areas. In the studied examples, we found that it outperforms the fixed-form neighbourhood used in classical MRF classifications. The advantage of having a neighborhood shape that automatically adapts to the image segment clearly appears in these cases of images containing fine elements, lanes or thin fields, but also complex natural landscape structures.
KEYWORDS: Lab on a chip, Vegetation, Data acquisition, Reflectivity, Modeling, Remote sensing, Data modeling, Environmental sensing, Information theory, Calibration
The detection of changes affecting continental surfaces has important applications in hydrological, meteorological, and climatic modelling, so that numerous change indices have already been proposed that use remote sensing data. In this work, we show the interest of combining several of them to improve change detection performance. The combination is done in the Dempster-Shafer evidence theory framework, therefore allowing ignorance modelling. Each mass function is defined either based on the result of the corresponding mono-index analysis, that is done using an 'a contrario' approach, or from generic sigmoid function in the absence of pdf assumption. Using actual SPOT/HRV data, we analyse the performance of different change indices, and their combination in different application cases: forest fires, forest logging either in pine forest or in mixed forest, and winter vegetation cover of fields in intensive farming areas. Finally, we also show the interest of the indices derived from the Information Theory, some of which being original.
As vegetation time evolution is one of the most relevant information to discriminate the different land cover types, land cover classification requires both temporal and spatial information. Due to the physical properties of remote sensors, this temporal information can only be derived from coarse resolution sensors such as MERIS (300×300 m2 pixel size) or SPOT/VGT (1 km2 pixel size). In this paper, we propose to use jointly high and coarse spatial resolution to perform an efficient high resolution land cover classification. The method is based on Bayesian theory and on the linear mixture model permitting, through a simulated annealing algorithm, to perform a high resolution classification from a coarse resolution time series.
This paper discusses the potential of radar signal to characterize the bare surface roughness in arid or semi-arid regions. Two different scales have been considered with two microwave sensors: high resolution ERS/SAR and low resolution ERS Wind Scatterometer instruments. Ground truth measurements were acquired over different arid sites in the South of Tunisia. An empirical approach is proposed to derive the surface roughness from SAR measurements. In this approach, the surface roughness is characterized by a parameter called Zs. Then, a good correlation between SAR and WSC data is demonstrated. Using these two sensors, we are able to derive the backscattering signal versus incidence angle, in the cases of big sand dunes, rocky relief and others. An empirical model is then proposed to retrieve the sand dune percentage within the different cells of the WSC.
The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. The main advantage of unsupervised classification is that no a priori information is needed. Dempster-Shafer formulation allows the user to consider union of classes, and to represent both imprecision and incertitude. So, it provides better representation of sensor information and more reliable classification results. An unsupervised multisource classification algorithm is applied to Mac- Europe'91 multi-sensor airborne campaign data. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths L and C bands) are compared. Performance of data fusion has been evaluated in terms of identification of culture types. Particularly, we show that, even if most performing results were achieved using the three data sets, good identification rates could be obtained using less expensive combinations of data.
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