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A statistical model for detecting changes in remote sensing images has recently been proposed in (Prendes et al., 2014a,b). This model is sufficiently general to be used for homogeneous images acquired by the same kind of sensors (e.g., two optical images from Pléiades satellites, possibly with different acquisition conditions), and for heterogeneous images acquired by different sensors (e.g., an optical image acquired from a Pléiades satellite and a synthetic aperture radar (SAR) image acquired from a TerraSAR-X satellite). This model assumes that each pixel is distributed according to a mixture of distributions depending on the noise properties and on the sensor intensity responses to the actual scene. The parameters of the resulting statistical model can be estimated by using the classical expectation-maximization (EM) algorithm. The estimated parameters are finally used to learn the relationships between the images of interest, via a manifold learning strategy. These relationships are relevant for many image processing applications, particularly those requiring a similarity measure (e.g., image change detection and image registration). The main objective of this paper is to evaluate the performance of a change detection method based on this manifold learning strategy initially introduced in (Prendes et al., 2014a,b). This performance is evaluated by using results obtained with pairs of real optical images acquired from Pléiades satellites and pairs of optical and SAR images.

A statistical model for detecting changes in remote sensing images has recently been proposed in (Prendes et al., 2014, 2015). This model is sufficiently general to be used for homogeneous images acquired by the same kind of sensors (e.g., two optical images from Pléiades satellites, possibly with different acquisition conditions), and for heterogeneous images acquired by different sensors (e.g., an optical image acquired from a Pléiades satellite and a synthetic aperture radar (SAR) image acquired from a TerraSAR-X satellite). This model assumes that each pixel is distributed according to a mixture of distributions depending on the noise properties and on the sensor intensity responses to the actual scene. The parameters of the resulting statistical model can be estimated by using the classical expectation-maximization (EM) algorithm. The estimated parameters are finally used to learn the relationships between the images of interest, via a manifold learning strategy. These relationships are relevant for many image processing applications, particularly those requiring a similarity measure (e.g., image change detection and image registration). The main objective of this paper is to evaluate the performance of a change detection method based on this manifold learning strategy initially introduced in (Prendes et al., 2014, 2015). This performance is evaluated by using results obtained with pairs of real optical images acquired from Pléiades satellites and pairs of optical and SAR images. [ABSTRACT FROM AUTHOR]

Knowing the distribution of vectors of zoonoses is fundamental to understand epidemiological dynamics and characterize risk areas. This distribution can be assessed at different scales: regionally, the range of these species defines the limits of occurrence of pathogen transmission and, locally, niches determine the hazard. This latter scale requires a very precise knowledge of the areas that can be gained through the analysis of very high spatial resolution satellite images. As part of a research project on leptospirosis in La Réunion (LeptOI), this study proposes to measure the potential of a Pleiades image (product resampled to to 50 cm) to characterize the local distribution of various small terrestrial mammals in Saint-Paul swamp area. Three trapping campaigns have led to the capture of 134 animals belonging to four of the five species occurring in Reunion Island (Rattus rattus, R. norvegicus, Suncus murinus and Tenrec ecaudatus). An objectbased image analysis of the Pleiades image allowed discriminating ten land use classes with a total accuracy of 83.6%. Landscape indices were calculated from the classification around each trap (shortest distances to each land use class, edge densities and proportion of each patch within buffers defined by a 50 meters or a 100 meters radius). Three areas ("wet", "urbanized" and "dry") are clearly discriminated by these variables, which can be used as ecological determinants of different epidemiological profiles. The wetland area is known as suitable to leptospirosis whereas the urbanized area is a place of co-occurrence of the four species and thus potentially favorable to the exchange of pathogens. [ABSTRACT FROM AUTHOR]

In 2009 a preliminary feasability study on the fourth ring road of Alger was carried out based on SPOT5 images. A comparative study was also conducted with panchromatic (PAN) and multispectral (MS) Pléiades images. The focus of the studies has distinguished six different geological formations. The diversity of terrain encountered has enabled the comparison of parameters such as the resolution and the spectral band. From a geological point of view at macroscopic scale, both satellites enable an aerial sketch mapping of the lineaments. However, a more precise structural mapping was observed with Pléiades PAN images. For SPOT5 and Pléiades MS images, even though their resolution is quite similar (respectively 2,5 m and 2,8 m), a stronger spectral contrast was observed with Pléiades MS which have enabled to highlight the different lithologies. For smaller geological details (1 m), Pléiades PAN images have given good results. Hence, because preliminary studies have to be the most advanced possible, it is clear that Pléiades images are the one that can lead to an exhaustive mapping of the geomorphology encountered. [ABSTRACT FROM AUTHOR]