Sonia Bouzidi

Image Processing for Sequences of Oceanographic Images

This paper is concerned with three main problems of image processing occuring on temporal sequences of satellite oceanographic images: approximate localization of the interesting structures on the images; segmentation or determination of the boundary of the structures; and temporal tracking of these boundaries to illustrate their evolution. In this paper, application is done on vortices and results are displayed all over the paper. For this purpose, we propose a new method for optical flow computation and interpretation and a geometric modelling of the structures. Oceanographic images obtained from environmental satellite platforms present a new challenge in computer science. The huge amount of data collected each day and the need for characterizing some specific structures on these images for oceanographic monitoring justify our approach for the detection and tracking of vortices on oceanographic images.

A remote sensing data fusion approach to monitor agricultural areas

Describes a fusion process between two different data sources, one providing an accurate spatial information, the other providing time series with a much coarser spatial scale. It is applied in the following remote sensing context: the forecast of cereals production, which is a challenging application of the new generation of Earth observation satellites. These two data types are required since agronomical models must be fed with a daily sampling of cereals reflectances, and since in Europe, fields have a relatively small size. SPOT-XS is wed to provide spatial information at the parcels level, a meso-scale sensor (here, NOAA-AVHRR), which outputs images of large areas every day, provides the temporal information. The combination of these two data sources makes it possible to daily estimate reflectances of main cultivations at the parcels level. The selected approach is as follows: a preliminary learning stage provides the reflectances of each type of cultivation; then operational scenarios are defined to apply the learning information in order to estimate statistics on large areas: using only one SPOT-XS image and meso-scale daily images, a fusion scheme makes it possible to obtain land use identification at high spatial resolution with its temporal behavior.

Vortex segmentation on satellite oceanographic images

Sea temperature images coming from infrared sensors (AVHRR) allow the visualization of oceanographic activity at meso-scale (100 km), such as temperature fronts, vortices, ... These phenomena are, by their very nature, deformable, and deserve specific studies. We focused our study on the segmentation of vortices. We present three different methods to do this, using hybrid hyperquadric functions, Markovian framework, and geometrical modelization.

Subpixel mixture modeling applied for vegetation monitoring

The NOAA-AVHRR sensor provides daily acquisitions which may be used for vegetation growth monitoring. However, the coarse resolution of these data causes a problem of pixel heterogeneity. The pixel radiometric value is, in fact, a composition of individual responses from the different land covers found within the pixel’s surface. In this paper we assume a linear relation. The simultaneous use of NOAA-AVHRR temporal series and of high spatial resolu- tion ground data makes it possible to estimate the pure NOAA reflectances in the visible and near infrared wavelengths for each vegetation type. These val- ues are then combined to compute the NDVI temporal profile which describes the seasonal cycle of the studied crops. The proposed method is applied on the region of Chartres in order to describe the major crops temporal behavior.

An approach for land cover change detection using low spatial resolution data

The paper investigates an approach for detecting land cover changes on low spatial resolution data. On a reference year, local curves describing the temporal reflectance in the visible and near Infrared related to each land cover type are computed from low spatial resolution data. Temporal profiles of NDVI are calculated and analysed to extract relevant features characterizing them for each land cover type. The same process is then applied regularly on the same site for the next years. The observation in the same features representation space allows detection of potential local change.

Hydric stress detection through actual evapotranspiration by remote sensing in semi-arid catchments

This paper addresses the characterization of Land Surface Temperature (LST) variability according to Land Cover. It is a first step of a study which concerns the extraction of hydrological parameters in a semi-arid catchment applied located in Southern-Africa, and which includes image processing of satellite data. The main applicative interest of this work is to make available profiles of evapotranspiration (ET), which can be derived from LST, and to detect hydric stress by comparison between profiles of ET: potential ET simulated by an hydrological model and that estimated by satellite measurements. LST can be daily computed using the two thermal bands of NOAA/AVHRR. However, due to its coarse resolution (1.1 km at nadir), a NOAA/AVHRR pixel includes several land cover types and LST cannot be linked to a particular component. So, we process a data fusion between NOAA/AVHRR acquisitions and one high resolution land-use classification derived from Landsat-TM (30 meters at nadir), and consider a physical-based mixture model of the temperature pixel. Inverting this model on a learning area outputs individual temporal profiles of LST for each land cover type: bare soil, vegetated surface (grass, arable land, forest...). The obtained results with Landsat classification are then used to generate LST maps at spatial resolution of 30 meters and with a daily frequency.

An operational approach to monitor vegetation using remote sensing

This paper addresses vegetation monitoring in European agricultural areas using Earth Observation satellites. Due to the small size of typical European fields, two complementary sensors are used, SPOT and NOAA-AVHRR, bringing the spatial and the temporal information respectively. A sub-pixel analysis of NOAA data using one SPOT image is performed to characterize fields with high spatial and temporal resolutions. To be used in an operational context, the method must have realistic data requirements. We define an operational scenario making use of only one SPOT image per site and a one year NOAA sequence, covering a large part of Europe. We first proceed to an unsupervised segmentation of the SPOT image; the NOAA data analysis on test sites provides the temporal evolution of vegetation; then, identification of fields is performed by minimizing a cost function measuring the similarity between the global reflectance observed on NOAA pixels and the reflectance computed from corresponding regions at SPOT resolution.

Use of multisensor, multiscale, and temporal data for segmenting vegetation

The study of vegetation repartition and evolution is a wide research field and application task for environmental modeling. The objective of our study is to discriminate different vegetation types by their temporal evolution. For that purpose, we use two different sensors: the SPOT sensor provides monthly data with a sufficient spatial resolution, while the NOAA sensor provides daily data, but with a poor spatial resolution. Combining these two complementary sensors seems to be a promising way to lead the study. We propose here a three-step experiment showing that the simultaneous use of these two sensors allows us to obtain a fine segmentation of land cover.