Jean-Paul Rudant

Coastline detection by a Markovian segmentation on SAR images

Abstract This paper presents a new method for extracting coastline in SAR images. It proposes a hierarchical approach using two Markov Random Fields (MRFs). At coarse resolution an MRF on a holes topology reduces the drawbacks affecting classical approaches (i.e., over-segmentations). The initial segmentation is then projected onto the full resolution image and a second classification is achieved using an Ising model. Applications on ERS-1 SAR images demonstrate the performance of the method.

Geographical and environmental approaches to urban malaria in Antananarivo (Madagascar)

BackgroundPrevious studies, conducted in the urban of Antananarivo, showed low rate of confirmed malaria cases. We used a geographical and environmental approach to investigate the contribution of environmental factors to urban malaria in Antananarivo.MethodsRemote sensing data were used to locate rice fields, which were considered to be the principal mosquito breeding sites. We carried out supervised classification by the maximum likelihood method. Entomological study allowed vector species determination from collected larval and adult mosquitoes. Mosquito infectivity was studied, to assess the risk of transmission, and the type of mosquito breeding site was determined. Epidemiological data were collected from November 2006 to December 2007, from public health centres, to determine malaria incidence. Polymerase chain reaction was carried out on dried blood spots from patients, to detect cases of malaria. Rapid diagnostic tests were used to confirm malaria cases among febrile school children in a school survey.A geographical information system was constructed for data integration. Altitude, temperature, rainfall, population density and rice field surface area were analysed and the effects of these factors on the occurrence of confirmed malaria cases were studied.ResultsPolymerase chain reaction confirmed malaria in 5.1% of the presumed cases. Entomological studies showed An. arabiensis as potential vector. Rice fields remained to be the principal breeding sites. Travel report was considered as related to the occurrence of P. falciparum malaria cases.ConclusionGeographical and environmental factors did not show direct relationship with malaria incidence but they seem ensuring suitability of vector development. Absence of relationship may be due to a lack of statistical power. Despite the presence of An. arabiensis, scarce parasitic reservoir and rapid access to health care do not constitute optimal conditions to a threatening malaria transmission. However, imported malaria case is suggestive to sustain the pocket transmission in Antananarivo.

Spatio-temporal monitoring of evaporitic processes using multi-resolution C band radar remote sensing data: example over the Chott el Djerid

This paper illustrates the contribution of spatio-temporal multiresolution spaceborne radar data for the monitoring of land surfaces. More precisely, it illustrates the potential of C-band spaceborne radar data, by using in synergy scatterometer and Synthetic Aperture Radar (SAR) sensors, for the spatio-temporal monitoring of evaporitic processes over a vast playa, the Chott el Djerid, located in central Tunisia. Scatterometer data from the Advanced Scatterometer (ASCAT) instrument are characterized by a high temporal frequency of acquisition, about 3 days over the chott, with a spatial resolution of 25 km. It is well suited for an interpretation of radar temporal signatures in relationship with seasonal variations of surface states. SAR images obtained from both Advanced SAR (ASAR) and RADARSAT-2 sensors are less frequent (about 20 days) but provide a higher spatial resolution, allowing for the discrimination of spatial patterns related to evaporitic processes. ASAR Wide Swath mode, associated with 150 m of spatial resolution, allows for the monitoring of the whole chott area, whereas RADARSAT-2, realizing full polarimetric acquisitions with a spatial resolution of 8 m, allows for the discrimination of finer spatial patterns over a sub-area within the chott. Both scatterometer and SAR data show an overall good agreement in radiometry. Polarimetry, available for the RADARSAT-2 data, allows for the highlighting of striking spatial patterns in relation with the various sedimentation processes within the saline deposit over the chott.

Developing a lowcost Vertical Take Off and Landing Unmanned Aerial System for centimetric monitoring of biodiversity the Fontainebleau Forest case

Monitoring vegetation dynamics is a very important task for biodiversity conservation, especially in the Global Change context where many natural habitats are threatened with extinction or reduced to small areas. Remote sensing (aerial or spatial) has played a key role in this monitoring for years. With space imagery, the main limitation is actually due to the metric resolution which is not well adapted for estimating the processes of changes occurring in areas where the number species, visible only in few places, is shrinking, like in the heathland habitat in Fontainebleau Forest case. For aerial imagery, the main problem is the cost of a specific mission or the availability of the plane or the pilot. We propose in this article another solution: the use of a home-made Unmanned Vertical Take Off and Landing Aerial System, which is a good compromise between the spatial and aerial solutions, and test the validity and the robustness of the tools developed for the management of natural habitat on a specific area of the Fontainebleau Forest.

Unmixing polarimetric radar images based on land cover type before target decomposition

A new method for unmixing radar polarimetric images with optical images is proposed. It was found that the polarimetric covariance matrix can be unmixed considering a linear model. As a result, this model is used to produce unmixed covariance matrices based on land cover types. We hope to prove that this unmixing of the polarimetric information produce greater information for land cover classification.

Faucon noir UAV project development of a set of tools for managing, visualizing and mosaicing centimetric UAV images

Eager for greater flexibility, and lesser costs, we are often tempted to acquire high-resolution data with home-made acquisition systems. However, this approach cannot benefit from usual tools to tackle problems such as image navigation within a large flow of very high resolution pictures, data completion and image mosaicing of small patches of the overflown area. This article presents the tools suite our laboratory has developed. In our case, we have developed homemade quad-rotor for low altitude imagery, equipped with up to three compact cameras on a stabilized platform. This configuration represents 2800 images at a ground resolution of 2 to 5 centimeters for each 20 minute flight. In order to take advantage of this flow of data, we needed a relevant set of tools for optimal acquisition and exploitation of the images.

Fusion of LiDAR and radar data for land-cover mapping in natural environments

Land-cover geodatabases are key products for the understanding of environmental systems and for setting up national and international prevention and protection policies. However, their automatic generation and update remain complicated with high accuracy over large scales. In natural environments, most of the existing solutions are semi-automatic in order to achieve a suitable discrimation of the large number of forest and crop classes. A large amount of remote sensing possibilities is at the moment available and data fusion appears to be the most suitable solution for that purpose. The paper tackles the issue of land-cover mapping in such areas assuming the existence of a partly non-updated 5-class geodatabase: buildings, roads, water, crops, forests. Lidar point clouds and Radar images at two spatial resolutions and bands are merged at the feature level and fed into an efficient supervised classification framework. Results show that some classes benefit from the joint exploitation of multiple observations in terms of accuracy or recall.

UN-sensored very high resolution Land-Cover mapping

Land-Cover databases (LC-DB) are mandatory for environmental purposes, but need to be regularly updated to provide robust and instructive spatial indicators. Moreover, an increasing number of sensors, such as optical and SAR satellite images or Lidar point cloud, allow to cover large areas regularly, and with a very high precision. Thus, automatic methods have to be developed to take into account the complementarity of available observations. In this paper, several fusion methods are proposed and introduced in an existing Land-Cover mapping framework. Those methods are compared on several scenarii (based on optical, SAR and Lidar datasets), and evaluated thanks to a very high resolution LC-DB.

Contribution of textural information from TerraSAR-X image for forest mapping

This study evaluates the potential of High Resolution Spotlight TerraSAR-X image for forest type discrimination. Emphasis is put on textural analysis accessible with high resolution radar data. Textural attributes are extracted from GLCM matrices, wavelet, and Fourier Transform (i.e. FOTO method). Their contribution for classification is assessed by their performance through the SVM algorithm.

Assessment of the relevance of information derived from the unmixing of polarimetric radar images

A new method to unmix radar polarimetric images with optical images was proposed. This method has pointed out that the unmixing model is able to split off polarimetric information on a land cover type basis. In this paper unmixed radar polarimetric images obtained are compared with the observed ones in non-mixed conditions. Then, Cloude and Pottier decomposition is performed on the unmixed and observed radar images to asses whether the understanding of physical scattering mechanisms is improved with the unmixing. Finally, a classification experiment is designed to determine whether this fusion framework make the transfer of information from the optical images to the unmixed radar images possible.