Orfeo ToolBox is an open-source project for state-of-the-art remote sensing, including a fast image viewer, applications callable from command-line, Python or QGIS, and a powerful C++ API. This article is an introduction to the Orfeo ToolBox's flagship features from the point of view of the two communities it brings together: remote sensing and software engineering.
Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 2015,7, mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10-20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the "Sentinel-2 for Agriculture" project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of "Sentinel-2 like" time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the "Joint Experiment for Crop Assessment and Monitoring" network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future "Sentinel-2 for Agriculture" system.
Bioluminescence imaging (BLI) allows detection of biological functions in genetically modified cells, bacteria, or animals expressing a luciferase (i.e., firefly, Renilla, or aequorin). Given the high sensitivity and minimal toxicity of BLI, in vivo studies on molecular events can be performed noninvasively in living rodents. To date, detection of bioluminescence in living animals has required long exposure times that are incompatible with studies on dynamic signaling pathways or nonanaesthetised freely moving animals. Here we develop an imaging system that allows: 1. bioluminescence to be recorded at a rate of 25 images/s using a third generation intensified charge-coupled device (CCD) camera running in a photon counting mode, and 2. coregistration of a video image from a second CCD camera under infrared lighting. The sensitivity of this instrument permits studies with subsecond temporal resolution in nonanaesthetized and unrestrained mice expressing firefly luciferase and imaging of calcium signaling in transgenic mice expressing green fluorescent protein (GFP) aequorin. This imaging system enables studies on signal transduction, tumor growth, gene expression, or infectious processes in nonanaesthetized and freely moving animals.
Abstract. Bioluminescence imaging (BLI) offers the possibility to study and image biology at molecular scale in small animals with applications in oncology or gene expression studies. Here we present a novel modelbased approach to 3D animal tracking from monocular video which allows the quantification of bioluminescence signal on freely moving animals. The 3D animal pose and the illumination are dynamically estimated through minimization of an objective function with constraints on the bioluminescence signal position. Derived from an inverse problem formulation, the objective function enables explicit use of temporal continuity and shading information, while handling important self-occlusions and time-varying illumination. In this model-based framework, we include a constraint on the 3D position of bioluminescence signal to enforce tracking of the biologically produced signal. The minimization is done efficiently using a quasi-Newton method, with a rigorous derivation of the objective function gradient. Promising experimental results demonstrate the potentials of our approach for 3D accurate measurement with freely moving animal.
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