A SAR image-based tool for prompt and effective earthquake response

Recently, a new concept for detection of damaged infrastructure after earthquakes has been introduced, based on analysis of double reflection lines in SAR images. This paper describes the development of a processing step for extraction of double-reflection lines, and its implementation. In particular, an unsupervised bright line detector working on the ratio of pre- and post-event single look complex SAR data is introduced, and is demonstrated using COSMO-SkyMed SAR data from the 2009 L'Aquila earthquake

Bright line detection in COSMO-SkyMed SAR images of urban areas

Bright lines are a characteristic feature of synthetic aperture radar (SAR) amplitude images of urban areas, and are commonly associated with man-made structures. In order to aid in the development of SAR applications using these features, an automated approach to bright line detection is proposed, based on scale-space ridge detection at a single scale, and using a naïve Bayesian classification step to select the ridge points corresponding to bright lines. The effectiveness of the technique is demonstrated by applying it to a COSMO-SkyMed image of L'Aquila, Italy

Remote sensing and crowd-sourcing

Collection of ground truth to validate remote sensing classification and/or detection algorithms is rarely accounted for due to the inaccessibility of the sites or the elevated costs of such operations. In this paper some of the opportunities behind crowd sourcing are explored through the description of a remote sensing project on water quality monitoring in Africa where the ground truth was collected involving and training people from local communities

Earthquake Damage Detection in Urban Areas using Curvilinear Features

Bright curvilinear features arising from the geometry of man-made structures are characteristic of synthetic aperture radar (SAR) images of urban areas, particularly due to double-reflection mechanisms. An approach to urban earthquake damage detection using double-reflection line amplitude change in single-look images has been established in previous literature. Based on this method, this paper introduces an automated tool for fast, unsupervised damage detection in urban areas. Ridge-based curvilinear features are extracted from a preevent SAR image, and double-reflection candidates are selected using prior probability distributions derived from a simple geometrical building model. The candidate features are then used with the ratio of a pair of single preevent and postevent SAR single-look amplitude images to estimate damage levels. The algorithm is very efficient, with overall computational complexity of $O(Nlog k)$ for an $N$-pixel image containing features of mean length $k$. The technique is demonstrated using COSMO-SkyMed data covering L'Aquila, Italy, and Port-au-Prince, Haiti

Remote Sensing and Crowd-Sourcing

Collection of ground truth to validate remote sensing classification and/or detection algorithms is rarely accounted for due to the inaccessibility of the sites or the elevated costs of such operations. In this paper some of the opportunities behind crowd sourcing are explored through the description of a remote sensing project on water quality monitoring in Africa where the ground truth was collected involving and training people from local communities