Analysis of Min-Trees over Sentinel-1 Time Series for Flood Detection

International audienceMonitoring flood is an important task for disaster management. It requires to distinguish between changes related to water from the other changes. We address such an issue by relying on both spatial and intensity information. To do so, we exploit min-tree that emphasize intensity extrema in a multiscale, efficient framework. We thus suggest a two-step approach operating on satellite image time series. We first perform a temporal analysis to identify images containing possible floods. Then a spatial analysis is achieved to detect flood areas on the selected images. Both steps relies on the analysis of component attributes extracted from the min-tree representation. We conduct some experiments on a flooded scene observed through Sentinel-1 SAR imagery. The results show that flood areas can be efficiently and accurately characterized with spatial component attributes extracted from hierarchical representations from SAR time series

SPATIO-TEMPORAL OBJECT STABILITY FOR MONITORING EVOLVING AREAS IN SATELLITE IMAGE TIME SERIES

International audienceMonitoring observable processes in Satellite Image Time Series (SITS) is one of the crucial way to understand dynamics of our planet that is facing unexpected behaviors due to climate change. In this paper, we propose a novel method to assess the evolution of objects (and especially their surface) through time. To do so, we first build a space-time tree representation of image time series. The so-called space-time tree is a hierarchical representation of an image sequences into a nested set of nodes characterizing the observed regions at multiple spatial and temporal scales. Then, we measure for each node the spatial area occupied at each time sample, and we focus on its evolution through time. We thus define the spatio-temporal stability of each node. We use this attribute to identify and measure changing areas in a remotely-sensed scene. We illustrate the purpose of our method with some experiments in a coastal environment using Sentinel-2 images, and in a flood occurred area with Sentinel-1 images

Attribute profiles for satellite image time series

International audienceMorphological attribute profiles have been one of the most effective image features for spatial-spectral classification of remote sensing images during the last decade. The motivation of this paper is to extend attribute profiles to satellite image time series, i.e. taking into account the temporal information. We introduce different approaches and report their performances for land cover mapping. Experiments are conducted on a Sentinel-2 dataset considering well-established supervised classification methods that are Random Forest and Support Vector Machines

Effects of nifedipine and Bay K 8644 on the R-PIA and caffeine-induced changes in the locomotor activity of rats

Possible interaction between adenosine and L type Ca2+ channel in the locomotor activity of rats was investigated, R-PIA (0.05 mg kg(-1)), an adenosine analogue, (20 mg kg(-1)), an adenosine receptor antagonist, significantly decreased locomotor activity, respectively. Ca2+ channel blocker nifedipine (5 mg kg(-1)) and the channel activator Bay K 8644 (0.5 mg kg(-1)) did not alter the locomotor activity. However, both drugs significantly potentiated the inhibitory effect of R-PIA on the locomotor activity. Additionally, caffeine induced increase in the locomotor activity was significantly blocked by nifedipine and Bay K 8644, This interaction might be due to the inhibitory effects of nifedipine and Bay K 8644 on the uptake of adenosine by rat brain. (C) 1996 The Italian Pharmacological Societ

Monitoring Urban Growth with Spatial Filtering of Satellite Image Time Series

International audienceMonitoring urban growth and change is an important task for urban planning and disaster management. While several change detection approaches have been proposed to deal with growing urban areas, their performances are usually limited due to outliers in Satellite Image Time Series (SITS). In this study, in order to discriminate urban growth from the other changes, we exploit spatial connectivity of the changed pixels. To do so, we first stack SITS to a single synthetic image whose pixel values denote the temporal variability along the series. Then, we propose to rely on efficient and well-established spatial filtering by means of the max-tree image representation, leading to a novel approach for detecting changes in urban areas, and more precisely focusing on the spatial extent of such changes in relationship with the urban growth. Experimental results obtained on Landsat imagery of Dar es Salaam showed that our approach helps to remove outliers from the change map and provides satisfactory accuracy

On Morphological Hierarchies for Image Sequences

Comparison of tree based representations for image sequencesInternational audienceMorphological hierarchies form a popular framework aiming at emphasizing the multiscale structure of digital image by performing an unsupervised spatial partitioning of the data. These hierarchies have been recently extended to cope with image sequences, and different strategies have been proposed to allow their construction from spatio-temporal data. In this paper, we compare these hierarchical representation strategies for image sequences according to their structural properties. We introduce a projection method to make these representations comparable. Furthermore, we extend one of these recent strategies in order to obtain more efficient hierarchical representations for image sequences. Experiments were conducted on both synthetic and real datasets, the latter being made of satellite image time series. We show that building one hierarchy by using spatial and temporal information together is more efficient comparing to other existing strategies

Torque and drag field applications in horizontal wells

The industry of drilling for oil, gas geothermal, and storage purposes necessitates construction of challenging wells in recent applications. Challenging wells are required to be designed prior to the drilling to selection of the equipment to be utilised while drilling. A poor engineering planning may result in losing time and effort to remediate torque and drag related troubles which might be encountered. Adequate planning is significantly important which is going to reveal possible drawbacks in planned well prior to commencing operations. The drag and torque calculations are required to be estimated and monitored accordingly whilst drilling of the wells. This paper gives the details of torque and drag calculations for every one-metre interval, so that the rig crew can see what is the theoretical calculated magnitudes and compare them to the actual. Implementation of this study is going to identify limitations due to torque and drag allowing safe drilling

Component trees for image sequences and streams

International audienceMorphological hierarchies now form a well-established framework for (still) image modeling and processing. However , their extension to time-related data remains largely unexplored. In this paper, we address such a topic and show how to analyze image sequences with tree-based representations. To do so, we distinguish between three kinds of models, namely spatial, temporal and spatial-temporal hierarchies. For each of them, we review different strategies to build the hierarchy from an image sequence. We also propose some algorithms to update such trees when new images are appended to the series and we compared the time complexity with tree building from scratch. We illustrate our findings with the max and min-tree structures built on grayscale data provided by Satellite Image Time Series that are gathering a growing interest in Earth Observation. Besides, we provide a comparative study for different hierarchies with classification experiments