Enhanced automatic detection of human settlements using Sentinel-1 interferometric coherence

This article presents an experiment in which multi-temporal interferometric coherence calculated from 6-days Sentinel-1A and Sentinel-1B image pairs and backscatter intensity σ° are jointly used for the extraction of built-up areas in the framework of the symbolic machine learning classification. The results obtained with the proposed approach confirm the enhanced capabilities of discriminating built-up areas when using coherence information in comparison to two available global human settlement layers derived: (1) from Landsat optical data and (2) from Sentinel-1 ground range detected data and based on backscatter intensity σ° only. The experiment carried out in The Netherlands Randstad area is expected to be indicative of the results obtainable for urban areas having similar structures and types of built-up.     

Multiscale quality assessment of Global Human Settlement Layer scenes against reference data using statistical learning

A method for quality assessment of the Global Human Settlement Layer scenes against reference data is presented. It relies on two settlement metrics; the local average and gradient functions that quantify the notions of settlement density and flexible settlement limits respectively. They are both utilized as generalization functions for increasing the level of abstraction of the sets under comparison. Generalization compensates for inaccuracies of the automatic target extraction method and can be computed at multiple scales. The comparison between the target built-up layers and the reference data employs an ordered multi-scale, linear regression computing the goodness of fit measure R²$R^2$. An optimized assessment procedure is investigated in a pilot study and is further employed in a big data exercise. A newly introduced quality metric returns the agreement between automatically extracted built-up from a set of 13605 scenes and the MODIS 500 urban layer, that was found too be as high as 91% for selected sensors. A final experiment attempts a performance increase at lower scales by correlating the target layer with automatically selected training subsets. At 50 m the adjusted R²$R^2$ increases by 3% with a mean squared error improvement of 2% compared to the performance achieved without statistical learning. The experiment suggests that the GHSL assessment at a global scale can be carried out based on limited high resolution reference data of minimal spatial coverage.     

Advances in mathematical morphology applied to geoscience and remote sensing

By concentrating on the analysis of the spatial relationships between groups of pixels, mathematical morphology provides us with an image processing strategy complementary to those based on the analysis of the spectral signature of single pixels. A wide variety of morphological transformations are available for extracting structural information in spatial data. Accordingly, a stream of successful applications in geoscience and remote sensing have been reported since the mid-1980s as highlighted in a brief survey. However, recent advances in the theory of mathematical morphology still remain largely unexplored. We show in this paper that they can enhance methodologies for the processing and analysis of Earth observation data for tasks as diverse as filtering, simplification, directional segmentation and crest line extraction. We also address important issues overlooked in the past and concerning the applicability of a given morphological filter to Earth observation data. In particular, we point out that self-dual or even self-complementary filters are required in many applications to produce results independent of the local contrast of the searched image structures.     

Estimating the velocity and direction of moving targets using a single optical VHR satellite sensor image

The present contribution demonstrates the feasibility and explores the limits of a new method for estimating the velocity and direction of moving targets using a single, optical, very‐high‐resolution (VHR) satellite sensor dataset. The method is based on the fact that there is a time lag between the data collection of the panchromatic (P) and multi‐spectral (MS) sensors in the same VHR platform. Consequently, it is developed around three main steps: (1) accurate image‐to‐image registration between MS and P images with a sub‐pixel displacement error, (2) precise location of barycentre of targets by mathematical morphology‐based image transforms, and (3) estimation of the ground velocity and direction of the target using the MS‐P spatial displacement, the known time lag, and an image‐to‐ground transformation taking into account the interior and exterior orientation of the sensors and a terrain height reference. An evaluation of the reliability and limits of the proposed method based on the observation of the results regarding manually selected moving and non‐moving targets is included.     

Textural analysis of coca plantations using remotely sensed data with resolution of 1 metre

The detection of surfaces cultivated with coca plants is essential to all activities related to monitoring and combating the production of illicit drugs such as cocaine. Moreover, deforestation processes can often be associated with the presence of coca plantations in the Andean region. In this context, the use of remote sensing technologies has proven to be effective for building an operational monitoring capacity, but more effort is needed to establish a robust computational frame for automatic detection procedures. This paper contributes to this effort by analysing the discrimination power of different textural measures and by proposing a new Mature Coca Index (MCI) derived from the textural analysis of 1‐m resolution satellite imagery. The approach can be applied to the latest generation optical sensors such as IKONOS and QUICKBIRD, and improves the current state‐of‐the‐art of recognition procedures for satellite data based on radiometric discrimination. The paper describes the MCI concept, its implementation, and application to a data sample derived from the IKONOS sensor.     

Multi scale Harris corner detector based on Differential Morphological Decomposition

In this paper, a novel method for multi scale corner analysis and detection is presented. First, state-of-the-art Harris-Laplace corner detector is reminded, which benefits from linear scale-space analysis. Secondly, a non-linear scale-space transform, namely Differential Morphological Decomposition, is described. This multi-scale transform is used jointly with the Harris corner indicator to build a new multi scale corner detector. Both corner detectors are visually assessed on synthetic and satellite images, highlighting the advantages of such a method.     

Intracranial hemorrhage in congenital factor II deficiency

A case of congenital defect of factor II is reported. It concerns a newborn with a not traumatic haematoma due to congenital hypoprothrombinaemia, which is rarely described in scientific literature.     

The recognition of road network from high‐resolution satellite remotely sensed data using image morphological characteristics

With the development of remote sensors and satellite technologies, high‐resolution satellite data such as IKONOS images have been available recently. By these new high‐resolution satellite data, remote sensing technologies can be successfully applied to more application areas such as extracting road network from high‐resolution satellite images. This paper proposes a newly developed approach to extract a road network from high‐resolution satellite images. The approach is based on the binary and greyscale mathematical morphology and a line segment match method. First, the outline of road network is detected based on the grey morphological characteristics. Then, the basic road network is detected by the line segment match method. Next, the detected basic road network is processed based on the knowledge about the roads and binary mathematical morphological methods. Finally, visual analysis and three indicators are used to evaluate the accuracy of the extracted road networks. The results of the accuracy evaluation demonstrate that the developed road network extraction approach can provide both good visual effect and high positional accuracy.