Scale Object Selection (SOS) through a hierarchical segmentation by a multi-spectral per-pixel classification

International audienceIn high resolution multispectral optical data, the spatial detail of the images are generally smaller than the dimensions of objects, and often the spectral signature of pixels is not directly representative of classes we are interested in. Thus, taking into account the relations between groups of pixels becomes increasingly important, making object­oriented approaches preferable. In this work several scales of detail within an image are considered through a hierarchical segmentation approach, while the spectral information content of each pixel is accounted for by a per­pixel classification. The selection of the most suitable spatial scale for each class is obtained by merging the hierarchical segmentation and the per­pixel classification through the Scale Object Selection (SOS) algorithm. The SOS algorithm starts processing data from the highest level of the hierarchical segmentation, which has the least amount of spatial detail, down to the last segmentation map. At each segmentation level, objects are assigned to a specific class whenever the percentage of pixels belonging to the latter, according to a pixel­based procedure, exceeds a predefined threshold, thereby automatically selecting the most appropriate spatial scale for the classification of each object. We apply our method to multispectral, panchromatic and pan­sharpened QuickBird images

Earthquake Damage Mapping Techniques Using SAR and Optical Remote Sensing Satellite Data

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Bias in food intake reporting in children and adolescents with type 1 diabetes: the role of body size, age and gender

An assessment of total daily energy intake is helpful in planning the overall treatment of children with type 1 diabetes (T1D). However, energy intake misreporting may hinder nutritional intervention.Aims: To assess the plausibility of energy intake reporting and the potential role of gender, body mass index (BMI) z-score (z-BMI), disease duration and insulin requirement in energy intake misreporting in a sample of children and adolescents with T1D.Methods: The study included 58 children and adolescents aged 8–16 yr with T1D. Anthropometry, blood pressure and glycated hemoglobin (HbA1c) were measured. Subjects were instructed to wear a SenseWear Pro Armband (SWA) for 3 consecutive days, including a weekend day and to fill out with their parents a weighed dietary record for the same days. Predicted energy expenditure (pEE) was calculated by age and gender specific equations, including gender, age, weight, height and physical activity level (assessed by SWA). The percent reported energy intake (rEI)/pEE ratio was used as an estimate of the plausibility of dietary reporting.Results: Misreporting of food intake, especially under-reporting, was common in children and adolescents with T1D: more than one-third of participants were classified as under- reporters and 10% as over-reporters. Age, z-BMI and male gender were associated with the risk of under-reporting (model R2 = 0.5). Waist circumference was negatively associated with the risk of over-reporting (model R2 = 0.25).Conclusions: Children and adolescents with T1D frequently under-report their food intake. Age, gender and z-BMI contribute to identify potential under-reporters

Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium resolution SAR data and identifiability techniques

Single satellite synthetic aperture radar (SAR) data are now regularly used to estimate hydraulic model parameters such as channel roughness, depth and water slope. However, despite channel geometry being critical to the application of hydraulic models and poorly known a priori, it is not frequently the object of calibration. This paper presents a unique method to simultaneously calibrate the bankfull channel depth and channel roughness parameters within a 2-D LISFLOOD-FP hydraulic model using an archive of moderate-resolution (150 m) ENVISAT satellite SAR-derived flood extent maps and a binary performance measure for a 30 × 50 km domain covering the confluence of the rivers Severn and Avon in the UK. The unknown channel parameters are located by a novel technique utilising the information content and dynamic identifiability analysis (DYNIA) (Wagener et al., 2003) of single and combinations of SAR flood extent maps to find the optimum satellite images for model calibration. Highest information content is found in those SAR flood maps acquired near the peak of the flood hydrograph, and improves when more images are combined. We found that model sensitivity to variation in channel depth is greater than for channel roughness and a successful calibration for depth could only be obtained when channel roughness values were confined to a plausible range. The calibrated reach-average channel depth was within 0.9 m (16 % error) of the equivalent value determined from river cross-section survey data, demonstrating that a series of moderate-resolution SAR data can be used to successfully calibrate the depth parameters of a 2-D hydraulic model

Star formation in young star cluster NGC 1893

We present a comprehensive multi-wavelength study of the star-forming region NGC 1893 to explore the effects of massive stars on low-mass star formation. Using near-infrared colours, slitless spectroscopy and narrow-band $H\alpha$ photometry in the cluster region we have identified candidate young stellar objects (YSOs) distributed in a pattern from the cluster to one of the nearby nebulae Sim 129. The $V, (V-I)$ colour-magnitude diagram of the YSOs indicates that majority of these objects have ages between 1 to 5 Myr. The spread in the ages of the YSOs may indicate a non-coeval star formation in the cluster. The slope of the KLF for the cluster is estimated to be $0.34\pm0.07$, which agrees well with the average value ($\sim 0.4$) reported for young clusters. For the entire observed mass range $0.6 < M/M_\odot \le 17.7$ the value of the slope of the initial mass function, $`\Gamma$', comes out to be $-1.27\pm0.08$, which is in agreement with the Salpeter value of -1.35 in the solar neighborhood. However, the value of $`\Gamma$' for PMS phase stars (mass range $0.6 < M/M_\odot \le 2.0$) is found to be $-0.88\pm0.09$ which is shallower than the value ($-1.71\pm0.20$) obtained for MS stars having mass range $2.5 < M/M_\odot \le 17.7$ indicating a break in the slope of the mass function at $\sim 2 M_\odot$. Estimated $`\Gamma$' values indicate an effect of mass segregation for main-sequence stars, in the sense that massive stars are preferentially located towards the cluster center. The estimated dynamical evolution time is found to be greater than the age of the cluster, therefore the observed mass segregation in the cluster may be the imprint of the star formation process. There is evidence for triggered star formation in the region, which seems to govern initial morphology of the cluster.Comment: Accepted for the publication in MNRAS, 21 pages, 26 figures, 10 table

Insight Into the Collocation of Multi-Source Satellite Imagery for Multi-Scale Vessel Detection

Ship detection from satellite imagery using Deep Learning (DL) is an indispensable solution for maritime surveillance. However, applying DL models trained on one dataset to others having differences in spatial resolution and radiometric features requires many adjustments. To overcome this issue, this paper focused on the DL models trained on datasets that consist of different optical images and a combination of radar and optical data. When dealing with a limited number of training images, the performance of DL models via this approach was satisfactory. They could improve 5-20% of average precision, depending on the optical images tested. Likewise, DL models trained on the combined optical and radar dataset could be applied to both optical and radar images. Our experiments showed that the models trained on an optical dataset could be used for radar images, while those trained on a radar dataset offered very poor scores when applied to optical images.Comment: 5 pages, accepted to IGARSS 202

Anti-asthmatics prescriptions in the paediatric population in the Lazio Region of Italy: association with socio-demographic children’s and physician’s characteristics

&nbsp; Background Asthma is the most common chronic disease in childhood; inItaly its prevalence is estimated to be 9% in children (0-14 years old). Objective Estimate the use of anti-asthmatics prescriptions in the paediatric population and to evaluate its association with children’s and physician’s characteristics. Methods The study was conducted in 728,830 children 1-14 years old residing in the Lazio region,Central Italy. Individual data on AA (ATC R03) prescriptions during 2009 were used. Prevalence was calculated according to children’s gender, age and area of residence. The association, in terms of rate ratio (RR), between AA prescription with children’s and physicians’ characteristics was estimated by multi level Poisson models. Results Overall, 404,239 AA prescriptions were given to 178,850 (25%) children with the highest frequency in the 1-2 age group (39%). Boys were more likely to receive a prescription than girls. Beclomethasone was the most prescribed active ingredient (34%), followed by salbutamol (24%); 44% of children ³6 years old had only 1 box prescription in the year, 48.9% of these subjects were treated with inhaled corticosteroids alone. Children’s gender, age and area of residence were the major determinants in drug prescription while, as far as physicians’ level, &nbsp;gender and number of patients in charge were associated to a greater probability of getting an AA prescription. Conclusion Prescription data provide useful information to measure prevalence use and consumption of AA drugs. Variability between age groups as well as differences in doctors’ characteristics suggests that specific strategies to optimise resource use of AA are needed

Towards a 20m global building map from Sentinel-1 SAR Data

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.Peer ReviewedPostprint (published version