Highly optimized weighted-IHS pan-sharpening with edge-preserving denoising

The interpretation of satellite imagery benefits from merging the spatial structure of the high-resolution panchromatic image with the spectral information. Such "pan-sharpening" has been the topic of extensive research. One objective of our investigations is to process satellite images within seconds. In this work, we build upon the "Fast IHS" technique, using a weighted linear combination of the up-sampled multispectral bands to derive a composite image closer to what the panchromatic sensor had seen. The difference to the actual panchromatic image approximates the high-frequency detail signal and is added to the multispectral bands. However, fixed band weights (exemplified by the "Modified IHS" algorithm) cannot account for differing radiometry and atmospheric conditions. To further reduce color distortion, we compute the optimal band weights for a given data set in the sense of minimizing the mean-square difference between the composite and panchromatic images. Since the noise in the panchromatic image (sometimes non-linear) impacts a subsequent graphbased segmentation algorithm, an additional denoising step is applied before fusion. We use an improved approximation of the Bilateral Filter, which preserves edges and requires only one fast iteration. The quality of the fused image is evaluated in a comparative study of pan-sharpening algorithms available in ERDAS IMAGINE 9.3. Objective metrics such as Q4 show an improvement in terms of color fidelity. The image segmentation results also demonstrate the applicability of this method towards automated image analysis

A multi-scale and multi-temporal hyperspectral target detection experiment - from design to first results

Hyperspectral target detection experiments under nonideal conditions are scarce. An extensive multi-scale and multi-temporal field experiment was designed towards the goal of knowledge expansion under such circumstances. A range of camouflage materials and specific targets of interest were placed in a realistic natural environment with vegetation cover and varying illumination. In several experiments, aspects like changes in the sun position, variable moisture, and relocations of targets were analysed. Using an aircraft-based and a drone-based imaging spectrometer, the target scenarios were mapped at different daytimes. The data were radiometrically, atmospherically and geometrically processed to allow subsequent data analysis. First insights deliver promising results

Experimental approach to camouflaged target detection and camouflage evaluation

This work discusses three individual camouflage experiments from a drone-based hyperspectral measurement campaign conducted in 2021. The experiments were designed to provide insight into different scenarios of camouflage classification and detection of camouflaged objects. The first experiment demonstrates an approach to detect different objects under camouflage using spectral unmixing. The second experiment presents the performance of commonly used hyperspectral classifiers for camouflage detection with respect to natural illumination changes throughout the day. Finally, the third experiment evaluates the effect of moisture on camouflage detection. For all experiments, we discuss the conditions under which hyperspectral data together with established detection and classification approaches can be used to robustly locate camouflage nets, and when detection is impaired

Hyperthun’22: A multi-sensor multi-temporal camouflage detection campaign

HyperThun’22 was a multi-sensor and multi-temporal camouflage detection campaign with drone-carried hyper- spectral, thermal, and RGB instruments. In more than 20 flights, various military targets were imaged with the purpose of analysing detection rates, camouflage trans- parency, and system performances. This article presents the campaign design, the data processing, and first data insights. Preliminary results show the potential of the acquired data for promising studies

An optical lattice clock with accuracy and stability at the 10−18 level

Progress in atomic, optical and quantum science has led to rapid improvements in atomic clocks. At the same time, atomic clock research has helped to advance the frontiers of science, affecting both fundamental and applied research. The ability to control quantum states of individual atoms and photons is central to quantum information science and precision measurement, and optical clocks based on single ions have achieved the lowest systematic uncertainty of any frequency standard. Although many-atom lattice clocks have shown advantages in measurement precision over trapped-ion clocks, their accuracy has remained 16 times worse. Here we demonstrate a many-atom system that achieves an accuracy of 6.4 × 10−18, which is not only better than a single-ion-based clock, but also reduces the required measurement time by two orders of magnitude. By systematically evaluating all known sources of uncertainty, including in situ monitoring of the blackbody radiation environment, we improve the accuracy of optical lattice clocks by a factor of 22. This single clock has simultaneously achieved the best known performance in the key characteristics necessary for consideration as a primary standard—stability and accuracy. More stable and accurate atomic clocks will benefit a wide range of fields, such as the realization and distribution of SI units11, the search for time variation of fundamental constants, clock-based geodesy and other precision tests of the fundamental laws of nature. This work also connects to the development of quantum sensors and many-body quantum state engineering (such as spin squeezing) to advance measurement precision beyond the standard quantum limit