Automated mapping of coastline from high resolution satellite images using supervised segmentation

International audienceIn this article, we are dealing with the problem of coastline extraction in High and Very High Resolution multispectral images. Locating precisely the coastline is a crucial task in the context of coastal resource management and planning. According to the type of coastal units (sandy beach, wetlands, dune, cliff), several definitions for the coastline has to be used. In this paper a new image segmentation method, which is not fully automated but relies on a low intervention of the expert to drive the segmentation process, is proposed. The method combines both a marker-based watershed transform (a standard image segmentation method) and a supervised pixel classification. The user inputs only consist of some spatial and spectral samples which are defined depending on the coastal environment to be monitored. The applicability of the method is tested on various types of coastal environments in France

An Interactive Algorithm for Image Smoothing and Segmentation

This work introduces an interactive algorithm for image smoothing and segmentation. A non-linear partial differential equation is employed to smooth the image while preserving contours. The segmentation is a region-growing and merging process initiated around image minima (seeds), which are automatically detected, labeled and eventually merged. The user places one marker per region of interest. Accurate and fast segmentation results can be achieved for gray and color images using this simple method

Automated detection of block falls in the north polar region of Mars

We developed a change detection method for the identification of ice block falls using NASA's HiRISE images of the north polar scarps on Mars. Our method is based on a Support Vector Machine (SVM), trained using Histograms of Oriented Gradients (HOG), and on blob detection. The SVM detects potential new blocks between a set of images; the blob detection, then, confirms the identification of a block inside the area indicated by the SVM and derives the shape of the block. The results from the automatic analysis were compared with block statistics from visual inspection. We tested our method in 6 areas consisting of 1000x1000 pixels, where several hundreds of blocks were identified. The results for the given test areas produced a true positive rate of ~75% for blocks with sizes larger than 0.7 m (i.e., approx. 3 times the available ground pixel size) and a false discovery rate of ~8.5%. Using blob detection we also recover the size of each block within 3 pixels of their actual size

Versatile and efficient pore network extraction method using marker-based watershed segmentation

© 2017 American Physical Society, http://dx.doi.org/10.1103/PhysRevE.96.023307Obtaining structural information from tomographic images of porous materials is a critical component of porous media research. Extracting pore networks is particularly valuable since it enables pore network modeling simulations which can be useful for a host of tasks from predicting transport properties to simulating performance of entire devices. This work reports an efficient algorithm for extracting networks using only standard image analysis techniques. The algorithm was applied to several standard porous materials ranging from sandstone to fibrous mats, and in all cases agreed very well with established or known values for pore and throat sizes, capillary pressure curves, and permeability. In the case of sandstone, the present algorithm was compared to the network obtained using the current state-of-the-art algorithm, and very good agreement was achieved. Most importantly, the network extracted from an image of fibrous media correctly predicted the anisotropic permeability tensor, demonstrating the critical ability to detect key structural features. The highly efficient algorithm allows extraction on fairly large images of 5003 voxels in just over 200 s. The ability for one algorithm to match materials as varied as sandstone with 20% porosity and fibrous media with 75% porosity is a significant advancement. The source code for this algorithm is provided

Waterpixels

International audience— Many approaches for image segmentation rely on a 1 first low-level segmentation step, where an image is partitioned 2 into homogeneous regions with enforced regularity and adherence 3 to object boundaries. Methods to generate these superpixels have 4 gained substantial interest in the last few years, but only a few 5 have made it into applications in practice, in particular because 6 the requirements on the processing time are essential but are not 7 met by most of them. Here, we propose waterpixels as a general 8 strategy for generating superpixels which relies on the marker 9 controlled watershed transformation. We introduce a spatially 10 regularized gradient to achieve a tunable tradeoff between the 11 superpixel regularity and the adherence to object boundaries. 12 The complexity of the resulting methods is linear with respect 13 to the number of image pixels. We quantitatively evaluate our 14 approach on the Berkeley segmentation database and compare 15 it against the state-of-the-art

Size distribution measurement of coal fragments using digital imaging processing

This paper focuses on the size distribution measurement of coal fragments by digital imaging processing. The fast and precise measurement of coal fragments, which is important to understand the crack propagation and energy dissipation process of coal failure, has not been achieved by previous research. In this paper, an image analysis method using MATLAB is proposed to measure fragment size distribution of coal fragments. The acquisition setup, analysis step and coding process for fragment size distribution measurement by digital imaging processing are introduced in detail. The statistical size distribution of coal fragments measured by image processing is compared with the theoretical distribution function and manual sieving results. This paper provides an innovative and efficient method for size distribution measurement in the study of coal failure process

A simple methodology to segment X-ray tomographic images of a multiphasic building stone.

International audienceAssessment of the weathering of a particular limestone, the tuffeau, used in historical monuments requires an accurate description of its microstructure. An efficient tools to obtain such a description is X-ray microtomography. However the segmentation of the images of this multiphasic material is not trivial. As the identification of pertinent markers of the structural components to extract is difficult, a two steps filtering approach is chosen. Alternate sequential filters are shown to efficiently remove the noise but, as they destroy the structural components smaller than the structuring element used, they cannot be carried out far enough. Hence as a second step in the filtering process, a mosaic operator, relying on a pragmatic yet efficient marker determination, is implemented to simplify further the images

Spectral-Spatial Classification of Hyperspectral Data based on a Stochastic Minimum Spanning Forest Approach

International audienceIn this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic Minimum Spanning Forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of Minimum Spanning Forests. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule, in order to build the final classification map. The proposed method is tested on three different data sets of hyperspectral airborne images with different resolutions and contexts. The influence of the number of markers and of the number of realizations M on the results are investigated in experiments. The performance of the proposed method is compared to several classification techniques (both pixelwise and spectral-spatial) using standard quantitative criteria and visual qualitative evaluation