Efficient Evaluation of the Number of False Alarm Criterion

This paper proposes a method for computing efficiently the significance of a parametric pattern inside a binary image. On the one hand, a-contrario strategies avoid the user involvement for tuning detection thresholds, and allow one to account fairly for different pattern sizes. On the other hand, a-contrario criteria become intractable when the pattern complexity in terms of parametrization increases. In this work, we introduce a strategy which relies on the use of a cumulative space of reduced dimensionality, derived from the coupling of a classic (Hough) cumulative space with an integral histogram trick. This space allows us to store partial computations which are required by the a-contrario criterion, and to evaluate the significance with a lower computational cost than by following a straightforward approach. The method is illustrated on synthetic examples on patterns with various parametrizations up to five dimensions. In order to demonstrate how to apply this generic concept in a real scenario, we consider a difficult crack detection task in still images, which has been addressed in the literature with various local and global detection strategies. We model cracks as bounded segments, detected by the proposed a-contrario criterion, which allow us to introduce additional spatial constraints based on their relative alignment. On this application, the proposed strategy yields state-of the-art results, and underlines its potential for handling complex pattern detection tasks

Breaking new ground in mapping human settlements from space -The Global Urban Footprint-

Today 7.2 billion people inhabit the Earth and by 2050 this number will have risen to around nine billion, of which about 70 percent will be living in cities. Hence, it is essential to understand drivers, dynamics, and impacts of the human settlements development. A key component in this context is the availability of an up-to-date and spatially consistent map of the location and distribution of human settlements. It is here that the Global Urban Footprint (GUF) raster map can make a valuable contribution. The new global GUF binary settlement mask shows a so far unprecedented spatial resolution of 0.4 arcsec ($\sim12 m$) that provides - for the first time - a complete picture of the entirety of urban and rural settlements. The GUF has been derived by means of a fully automated processing framework - the Urban Footprint Processor (UFP) - that was used to analyze a global coverage of more than 180,000 TanDEM-X and TerraSAR-X radar images with 3m ground resolution collected in 2011-2012. Various quality assessment studies to determine the absolute GUF accuracy based on ground truth data on the one hand and the relative accuracies compared to established settlements maps on the other hand, clearly indicate the added value of the new global GUF layer, in particular with respect to the representation of rural settlement patterns. Generally, the GUF layer achieves an overall absolute accuracy of about 85\%, with observed minima around 65\% and maxima around 98 \%. The GUF will be provided open and free for any scientific use in the full resolution and for any non-profit (but also non-scientific) use in a generalized version of 2.8 arcsec ($\sim84m$). Therewith, the new GUF layer can be expected to break new ground with respect to the analysis of global urbanization and peri-urbanization patterns, population estimation or vulnerability assessment

User-Assisted Image Shadow Removal

This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluation. Shadow masks are first detected by analysing user specified shadow feature strokes. Sample intensity profiles with variable interval and length around the shadow boundary are detected next, which avoids artefacts raised from uneven boundaries. Texture noise in samples is then removed by applying local group bilateral filtering, and initial sparse shadow scales are estimated by fitting a piece-wise curve to intensity samples. The remaining errors in estimated sparse scales are removed by local group smoothing. To relight the image, a dense scale field is produced by in-painting the sparse scales. Finally, a gradual colour correction is applied to remove artefacts due to image post-processing. Using state-of-the-art evaluation data, we quantitatively and qualitatively demonstrate our method to outperform current leading shadow removal methods