In this paper, a fast and flexible algorithm for computing watersheds in digital grayscale images is introduced. A review of watersheds and related notion is first presented, and the major methods to determine watersheds are discussed. The present algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using a queue of pixels. It is described in detail and provided in a pseudo C language. We prove the accuracy of this algorithm is superior to that of the existing implementations. Furthermore, it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images and even to graphs are straightforward. In addition, its strongest point is that it is faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for MR imagery and for digital elevation models. An example of 3-D watershed is also provided. Lastly, some ideas are given on how to solve complex segmentation tasks using watersheds on graphs.

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A general framework for processing high and veryhigh resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km² of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.Index Terms-Built-up density, CSL, global human settlement layer, linear regression, PANTEX, urban limits.

This paper introduces an image decomposition and simplification method based on the constrained connectivity paradigm. According to this paradigm, two pixels are said to be connected if they comply to a series of constraints defined in terms of simple measures such as the maximum grey level differences over well-defined pixel paths and regions. The resulting connectivity relation generates a unique partition of the image definition domain. The simplification of the image is then achieved by setting each segment of the partition to the mean value of the pixels falling within this segment. Fine to coarse partition hierarchies (and therefore images of increasing degree of simplification) are produced by varying the threshold value associated with each connectivity constraint. The paper also includes a generalisation to multichannel images, applications, a review of related image segmentation techniques, and pseudo-code for an implementation based on queue and stack data structures.