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• Key quality criteria for pixel-based composites identified and assessed. • New method for comprehensive and quantitative assessment of composites. • Over 16,000 Landsat imagery were mosaicked and analyzed over Europe. • Best Available Pixel (BAP) and Medoid composites were assessed. • BAP was more temporally consistent while Medoid was more radiometrically consistent. Remote sensing is one of the main sources of information for monitoring forest dynamics; however, surface reflectance is often not possible to accurately derive due to haze, cloud, or cloud shadow. Pixel-based composites are generated from multi-temporal images to cover the entire area of interest using several different methods. While the availability of free and open remote sensing data has further expanded the use of compositing approaches, to date a comprehensive methodology to assess the quality of these composites does not exist, nor is there a detailed set of compositing requirements to ensure consistent and reliable outputs to produce maps and statistics. Herein, we introduce a pixel-based composite assessment methodology based on five criteria: (i) number of valid observations and number of pixels with no available observations (data gaps), (ii) amount of unscreened clouds, cloud shadows, haze, or smoke (noise), (iii) radiometric consistency of the surface reflectance data, (iv) temporal proximity of pixels acquisition dates, and (v) spatial agreement of pixels acquisition dates. To test our methodology, we processed more than 16,000 Landsat images to generate and assess the Best Available Pixel (BAP) and the Medoid pixel-based composites for summer 2019 (2019-Jun-1 to 2019-Aug-31) over Europe, with a focus on the forested ecosystems. We found that BAP resulted in composites that were more temporally consistent, whereas the Medoid approach resulted in composites that were more radiometrically consistent. Our results illustrate that our assessment approach is effective for comprehensively assessing the quality of pixel-based composites and could be implemented when using composites to generate statistical estimates (i.e. forest area) and for assessing the performance of new compositing algorithms or for selecting an appropriate compositing approach for a specific application. [ABSTRACT FROM AUTHOR]

Multi-temporal satellite imagery can be composited over a season (or other time period) to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. For the purposes of vegetation monitoring, a commonly used technique is the Maximum NDVI Composite, used in conjunction with variety of other constraints. The current paper proposes an alternative based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values, and appears to be better at producing imagery which is representative of the time period. For each pixel, the medoid is always selected from the available dates, so the result is always a single observation for that pixel, thus preserving relationships between bands. The method is applied to Landsat TM/ETM+ imagery to create seasonal reflectance images (four per year), with the aim being a regular time series of reflectance values which captures the variability at seasonal time scales. Analysis of the seasonal reflectance values suggests that resulting temporal image composites are more representative of the time series than the maximum NDVI seasonal composite. [ABSTRACT FROM AUTHOR]

Averaging GPS trajectories is needed in applications such as clustering and automatic extraction of road segments. Calculating mean for trajectories and other time series data is non-trivial and shown to be an NP-hard problem. medoid has therefore been widely used as a practical alternative and because of its (assumed) better noise tolerance. In this paper, we study the usefulness of the medoid to solve the averaging problem with ten different trajectory-similarity/-distance measures. Our results show that the accuracy of medoid depends mainly on the sample size. Compared to other averaging methods, the performance deteriorates especially when there are only few samples from which the medoid must be selected. Another weakness is that medoid inherits properties such as the sample frequency of the arbitrarily selected sample. The choice of the trajectory distance function becomes less significant. For practical applications, other averaging methods than medoid seem a better alternative for higher accuracy. [ABSTRACT FROM AUTHOR]

The Mars Color Camera (MCC) of Indian Space Research Organisation (ISRO) captures the elusive moon of Mars, Phobos. MCC instrument is the eye of Mars Orbiter Mission (MOM) launched by ISRO on 05th November 2013 from the spaceport of India, Sriharikota. MCC has already provided more than 1000 images of Martian surface features covering different terrains. Recently, MCC has captured Phobos image at successive time instances on 01st July 2020. Multiple unpaired acquisitions of planetary remote sensing images often contain rich information to super resolve the spatial details of these images. This paper describes the techniques developed to enhance the Phobos image from MCC multi-frame acquisitions using image rectification and topographic data. In this regard, the co-registration of MCC frames is a crucial step. To co-register the data at sub-pixel level, we present a method, namely Random Sample Consensus (RANSAC) pruning based Scale Invariant Feature Transform (R-SIFT). The image composite is based on Medoid approach. It is further enhanced by contrast limited adaptive histogram equalization (CLAHE). The location of Phobos is computed using Spacecraft Planet Instrument Camera Matrix Event (SPICE) toolkit. For topographic correction, we rely on the Digital Elevation Model (DEM) of Phobos using publicly available resources. Visual inspection and image quality parameters, such as BRISQUE, RMSE, PSNR, and SSIM are estimated for both enhanced and topographically corrected image for assimilation purpose. After incorporating these techniques, the final Phobos image appears more representative, spatially enhanced, and has normalized radiometry to study its surface features. • Image enhancement using unpaired planetary remote sensing dataset. • Image processing pipeline of Indian Mars Color Camera. • Sub-Pixel image registration workflow. • Generation of Medoid composite. • Topographic correction framework for Phobos image. [ABSTRACT FROM AUTHOR]

Tertiary structure (3D) is the physical context of RNA regulatory activity. Retroviruses are RNA viruses that replicate through the proviral DNA intermediate transcribed by hosts. Proviral transcripts form inhomogeneous populations due to variable structural ensembles of overlapping regulatory RNA motifs in the 5′-untranslated region (UTR), which drive RNAs to be spliced or translated, and/or dimerized and packaged into virions. Genetic studies and structural techniques have provided fundamental input constraints to begin predicting HIV 3D conformations in silico. Using SimRNA and sets of experimentally-determined input constraints of HIVNL4-3 trans-activation responsive sequence (TAR) and pairings of unique-5′ (U5) with dimerization (DIS) or AUG motifs, we calculated a series of 3D models that differ in proximity of 5′-Cap and the junction of TAR and PolyA helices; configuration of primer binding site (PBS)-segment; and two host cofactors binding sites. Input constraints on U5-AUG pairings were most compatible with intramolecular folding of 5′-UTR motifs in energetic minima. Introducing theoretical constraints predicted metastable PolyA region drives orientation of 5′-Cap with TAR, U5 and PBS-segment helices. SimRNA and the workflow developed herein provides viable options to predict 3D conformations of inhomogeneous populations of large RNAs that have been intractable to conventional ensemble methods. [ABSTRACT FROM AUTHOR]