Road extraction from very high resolution sensors is a very popular topic in panchromatic and multispectral remote sensing image analysis. Despite the vast number of methods proposed in the literature to deal with this problem, in practice, most are quite limited and do not account for geometric and radiometric variability. Our aim is to propose a novel road extraction approach able to efficiently extract roads and reduce computation time using texture analysis and multiscale reasoning based on the beamlet transform. The proposed methodology consists of two stages: 1) road edge candidate selection and 2) multiscale reasoning with the beamlet transform. In the first step, mathematical morphology is applied to distinguish rectilinear structures, and road edge candidates are identified using the Canny edge detector. In the second phase, multiscale reasoning using the beamlet transform allows local and global information to be combined. Global information is introduced to distinguish main road axes at coarser scales, and local segments in finer scales, which are aggregated to reconstruct the road network. Rules based on the spatial relationships between segments belonging to different levels of resolution are also introduced at this stage. The experiments are performed based on the images acquired from the city of Port-au-Prince in Haiti during the earthquake of January 2010. The results demonstrate the accuracy and efficiency of our algorithm.

Nowadays, satellite images are considered as one of the most relevant sources of information in the context of major disasters management. Their availability in extreme weather conditions and their ability to cover wide geographic areas make them an indispensable tool toward an effective disaster response. Among the various available sensors, Synthetic Aperture Radar (SAR) is distinguished in the context of flood management by its ability to penetrate cloud cover and its robustness to unfavourable weather conditions. This work aims at developing a new technique for flooded areas extraction from high resolution time-series SAR images. The proposed approach is mainly based on three steps: first, homogeneous regions characterizing water surfaces are extracted from each SAR image using a local texture descriptor. Then, mathematical morphology is applied to filter tiny artifacts and small homogeneous areas present in the image. And finally, spatial and radiometric information embedded in each pixel are extracted and are fused with the same pixel information but from another image to decide if the current pixel belongs to a flooded region. In order to assess the performance of the proposed algorithm, our methodology was applied to time-series images acquired before and during three different flooding events: (1) Richelieu River and lake Champlain floods, Quebec, Canada in 2011; (2) Evros River floods, Greece in 2014 and (3) Western and southwestern of Iran floods in 2016. Experiments show that our approach gives very promising results compared to existing techniques.

Water bodies extraction using satellite images is of great importance due to its utility in several applications such as land use planning, floods management, and monitoring. Among the wide range of sensors orbiting the Earth, Synthetic Aperture Radar (SAR) is a very effective tool in this context due to its robustness in the face of unfavorable weather conditions and its cloud penetration capabilities. This paper presents a novel river extraction algorithm from high-resolution SAR images mainly based on the combination of a local texture measurement and the global knowledge associated with the shape of the object of interest. A local texture measurement is first computed at every pixel of the image to extract the homogeneous surfaces contained in the image, and then a mathematical morphology operator is applied to eliminate the noise generated by speckle characterizing SAR images. Finally, the surface occupied by the object of interest is compared with the surface associated with the smallest rectangle that encloses this object in order to separate rivers from lakes in the image. The proposed approach was tested on SAR images acquired by the RADARSAT-2 satellite over numerous regions of Canada. Our experimental results demonstrate that the proposed approach is robust and effective.

Infrastructures damage detection in case of major disasters is one of the most discussed problems and represent an active field of research in remotely sensed imaging. In this paper, a novel method designed for fast roads damage extraction is proposed since these structures are important in the delivery of assistance and to manage the intervention of the emergency teams on ground. The proposed methodology includes first an already completed step that consists in extracting the road network from both the pre- and post-disaster images. Then, a multiscale segmentation based on the wavelet transform is performed on the road surface and the obtained objects from the two coregistered images are compared. Finally, the Dempster Shafer theory is applied to decide the membership class of each object in a first step, and then identify the nature of changes using the multidimensional evidential reasoning. Images acquired by the Geo-Eye satellite before and after the earthquake that hits Port-au-Prince (Haiti) on January 2010 are used in the experiments.

Road extraction is a topical research because of complexity due to his large topological variability. Increasing the spatial resolution generates noise which makes extraction difficult, especially in case of major disaster in an urban context. This problem increases false alarm rates and generally affects the performance of road extraction algorithm. Our aim is to improve the quality of roads extraction after adaptation of the Lowe's SIFT descriptors (scale-invariant feature transform) jointly with spectral angle algorithm. The characterization is performed on two image at various resolution images, respectively representing a rural and urban disaster area, captured by Quickbird satellite. Our approach significantly reduces the amount of false detection and shows an overall accuracy of up to nearly 30% in some cases.

In this paper, a new method for road extraction from very high resolution satellite images is introduced. The proposed methodology is based on multiscale analysis of the image through beamlet transform that can approximate edges of an image by means of line segments appearing at different resolution scales. The hypothesis of the membership of these segments to a road is verified by identification of parallel beam-lets representing the main axes of the road. A reconstruction step of the road network is finally applied by aggregation of beamlets representing road candidates belonging to lower scales.