Improving shadows detection for solar radiation numerical models

[EN]Solar radiation numerical models need the implementation of an accurate method for determining cast shadows on the terrain or on solar collectors. The aim of this work is the development of a new methodology to detect the shadows on a particular terrain. The paper addresses the detection of self and cast shadows produced by the orography as well as those caused by clouds. The paper presents important enhancements on the methodology proposed by the authors in previous works, to detect the shadows caused by the orography. The domain is the terrain surface discretised using an adaptive mesh of triangles. A triangle of terrain will be under cast shadows when, looking at the mesh from the Sun, you can find another triangle that covers all or partially the first one. For each time step, all the triangles should be checked to see if there are cast or self shadows on it. The computational cost of this procedure eventually resulted unaffordable when dealing with complex topography such as that in Canary Islands thus, a new methodology was developed. This one includes a filtering system to identify which triangles are those likely to be shadowed. If there are no self shadowed triangles, the entire mesh will be illuminated and there will not be any shadows. Only triangles that have their backs towards the Sun will be able to cast shadows on other triangles. Detection of shadows generated by clouds is achieved by a shadow algorithm using satellite images. In this paper, Landsat 8 images have been used. The code was done in python programming language. Finally, the outputs of both approaches, shadows generated by the topography and generated by clouds, can be combined in one map. The whole problem has been tested in Gran Canaria and Tenerife Island (Canary Islands – Spain), and in the Tatra Mountains (Poland and Slovakia).Ministerio de Economía y Competitividad del Gobierno de Españ

Convolutional neural networks for on-board cloud screening

AcloudscreeningunitonasatelliteplatformforEarthobservationcanplayanimportant role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art

Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery

The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1 (GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery, which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible bands and one near-infrared band. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery. The MFC algorithm first implements threshold segmentation based on the spectral features and mask refinement based on guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with the texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated using 108 globally distributed scenes. The results indicate that MFC performs well under most conditions, and the average overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive analysis with the official provided cloud fractions, MFC shows a significant improvement in cloud fraction estimation, and achieves a high accuracy for the cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral bands. The proposed method could be used as a preprocessing step in the future to monitor land-cover change, and it could also be easily extended to other optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing of Environment, vol. 191, pp.342-358, 2017. (http://www.sciencedirect.com/science/article/pii/S003442571730038X

The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping

One of the megatrends marking our societies today is the rapid growth of urban agglomerations which is accompanied by a continuous increase of impervious surface (IS) cover. In light of this, accurate measurement of urban IS cover as an indicator for both, urban growth and environmental quality is essential for a wide range of urban ecosystems studies. The aim of this work is to present an approach based on both optical and SAR data in order to quantify urban impervious surface as a continuous variable on regional scales. The method starts with the identification of relevant areas by a semi automated detection of settlement areas on the basis of single-polarized TerraSAR-X data. Thereby the distinct texture and the high density of dihedral corner reflectors prevailing in build-up areas are utilized to automatically delineate settlement areas by the use of an object-based image classification method. The settlement footprints then serve as reference area for the impervious surface estimation based on a Support Vector Regression (SVR) model which relates percent IS to spectral reflectance values. The training procedure is based on IS values derived from high resolution QuickBird data. The developed method is applied to SPOT HRG data from 2005 and 2009 covering almost the whole are of Can Tho Province in the Mekong Delta, Vietnam. In addition, a change detection analysis was applied in order to test the suitability of the modelled IS results for the automated detection of constructional developments within urban environments. Overall accuracies between 84 % and 91% for the derived settlement footprints and absolute mean errors below 15% for the predicted versus training percent IS values prove the suitability of the approach for an area-wide mapping of impervious surfaces thereby exclusively focusing on settlement areas on the basis of remotely sensed image data

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data.

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many others

Automated detection of aortic annulus sizing based on decision level fusion

Aortic valve disease occurs due to calcification on the area of leaflets and it is progressive over time. Surgical Aortic Valve Replacement (SAVR) can be performed to treat the patient. However, due to invasive procedure of SAVR, a new method known as Transcatheter Aortic Valve Implantation (TAVI) has been introduced, where a synthetic catheter is placed within the patient’s heart valve. Traditionally, aortic annulus sizing procedure requires manual measurement of scanned images acquired from different imaging modalities which are Computed Tomographic (CT) and echocardiogram where both of the modalities produce inconsistency in measuring the aortic annulus yet able to produce different parameters which lead to accurate measurement. In this research, the image processing techniques of CT scan and echocardiogram images are done separately in order to obtain the aortic annulus size. Intensity adjustment and median filter are applied to CT scan image pre-processing, Watershed Transformation associated with the morphological operation has been used to perform the aortic annulus segmentation while image resizing and wavelet denoising method have been performed in echocardiogram image pre-processing followed by the implementation of Otsu N-clustering and morphological operation method for object segmentation. Then, Euclidean distance formula is applied to measure the distance between two points that indicates the diameter of the aortic annulus. Finally, a decision fusion technique based on the mathematical statistic approach has been applied to fuse the measured annulus size obtained from both modalities. Results affirmed the approach’s ability to achieve accurate annulus measurements when the final results are compared with the ground truth. In addition, the application of non-probabilistic estimation on the decision level fusion approach which does not required the dataset training produces fast computational time and helps in determining the optimal size of new aortic valve to be implemented in human heart

Automatic Detection and Quantification of Bluff Erosion Events in Single Image Series

Many communities along coastlines and riverbanks are threatened by water erosion and hence an accurate model to predict erosion events is needed in order to plan mitigation strategies. Such models need to rely on readily available meteorological data that may or may not be correlated with the occurrence of erosion events. In order to accurately study these potential correlations, researchers need a quantified time series index indicating the occurrence and magnitude of erosion in the studied area. We show that such an index can be obtained by creating and analyzing a single image series using relatively cheap consumer grade digital cameras. These image series are naturally of lower quality and subject to a large amount of variability as environmental conditions change over time. We initially analyze each image as a whole and subsequently demonstrate the great advantages of segmenting each image. This allows for independent parallel processing of segments while preventing cross-contamination between them. Finally, we are able to automatically detect 67% of all erosion events while accepting only a small number of false positives