Search

Multispectral (MS) images provided by Earth observation satellites have generally a poor spatial resolution while panchromatic images (PAN) exhibit a spatial resolution two or four times better. Data fusion is a means to synthesize MS images at higher spatial resolution than original by exploiting the high spatial resolution of the PAN. This process is often called pansharpening. The synthesis property states that the synthesized MS images should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. The methods based on the concept Amélioration de la Résolution Spatiale par Injection de Structures (ARSIS) are able to deliver synthesized images with good spectral quality but whose geometrical quality can still be improved. We propose a more precise definition of the synthesis property in terms of geometry. Then, we present a method that takes explicitly into account the difference in modulation transfer function (MTF) between PAN and MS in the fusion process. This method is applied to an existing ARSIS-based fusion method, i.e., A trou wavelet transform-model 3. Simulated images of the sensors Pleiades and SPOT-5 are used to illustrate the performances of the approach. Although this paper is limited in methods and data, we observe a better restitution of the geometry and an improvement in all indices classically used in quality budget in pansharpening. We present also a means to assess the respect of the synthesis property from an MTF point of view. [ABSTRACT FROM AUTHOR]

The goals of the Heliosat Project of Ecole Nationale Supérieure des Mines de Paris are: (i) to design a system for the estimation of incident solar radiation at ground level from images of the Earth acquired by geostationary satellites, (ii) to produce detailed maps of the spatial distribution of the global radiation and (hi) to improve the system depending upon the results and progress in science. Among the numerous methods already developed, one of them has been selected for its accuracy as well as for its simplicity. It has been tested during 30 consecutive months beginning January 1983. Operations were performed in a completely automatic, unmanned fashion and trades-off between routine and accuracy have been adopted. Areas under concern were Europe and Central-West Africa (1984 only). This paper presents the results of the tests conducted during 1983. Measurements of sixteen pyronometers were compared three times a day to the estimates of the selected method. Different comparisons are presented. The error in the reconstruction of both the instantaneous hourly global radiation Gh and the monthly average of Gh is less than 0.06kwh/m2. When compared to other methods, Heliosat ranks as one of the most accurate. [ABSTRACT FROM PUBLISHER]

Magnetic nanoparticles (MNPs) are used extensively across numerous disciples, with applications including Magnetic Particle Imaging (MPI), targeted hyperthermia, deep brain stimulation, immunoassays, and thermometry. The assessment of MNPs, especially those being designed for MPI, is performed with magnetic particle spectrometers, relaxometers, loop tracers, or similar devices. Despite the many applications and the need for particle assessment, there are few consolidated resources for designing or building such a MNP assessment system. Here, we describe the design and performance of an open-source device capable of spectroscopy, relaxometry, and loop tracing. We show example measurements from the device and quantify the detection sensitivity by measuring a dilution series of Synomag-D 70 nm (from 0.5 mg Fe/ml to 7 ng Fe/ml) with a 10 mT drive field at 23.8 kHz. The device measures 260 pg Fe with SNR = 1 and 1.3 ng at SNR = 5 in spectroscopy mode in under one second of measurement time. The system has a dynamic range of 60 μg to 260 pg Fe without changing the hardware configuration. As an example application, we characterize Synomag-D's relaxation time constant for drive fields 2–18 mT and compare the magnetization responses of two commonly used MNPs. [ABSTRACT FROM AUTHOR]