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Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture.

Zooplankton plays a major role in ocean food webs and biogeochemical cycles, and provides major ecosystem services as a main driver of the biological carbon pump and in sustaining fish communities. Zooplankton is also sensitive to its environment and reacts to its changes. To better understand the importance of zooplankton, and to inform prognostic models that try to represent them, spatially-resolved biomass estimates of key plankton taxa are desirable. In this study we predict, for the first time, the global biomass distribution of 19 zooplankton taxa (1-50 mm Equivalent Spherical Diameter) using observations with the Underwater Vision Profiler 5, a quantitative in situ imaging instrument. After classification of 466,872 organisms from more than 3,549 profiles (0-500 m) obtained between 2008 and 2019 throughout the globe, we estimated their individual biovolumes and converted them to biomass using taxa-specific conversion factors. We then associated these biomass estimates with climatologies of environmental variables (temperature, salinity, oxygen, etc.), to build habitat models using boosted regression trees. The results reveal maximal zooplankton biomass values around 60°N and 55°S as well as minimal values around the oceanic gyres. An increased zooplankton biomass is also predicted for the equator. Global integrated biomass (0-500 m) was estimated at 0.403 PgC. It was largely dominated by Copepoda (35.7%, mostly in polar regions), followed by Eumalacostraca (26.6%) Rhizaria (16.4%, mostly in the intertropical convergence zone). The machine learning approach used here is sensitive to the size of the training set and generates reliable predictions for abundant groups such as Copepoda (R2 ≈ 20-66%) but not for rare ones (Ctenophora, Cnidaria, R2 < 5%). Still, this study offers a first protocol to estimate global, spatially resolved zooplankton biomass and community composition from in situ imaging observations of individual organisms. The underlying dataset covers a period of 10 years while approaches that rely on net samples utilized datasets gathered since the 1960s. Increased use of digital imaging approaches should enable us to obtain zooplankton biomass distribution estimates at basin to global scales in shorter time frames in the future.

A growing literature supports maximal safe resection followed by standard combined chemoradiotherapy (i.e. maximal first-line therapy) for selected elderly glioblastoma patients. To assess the prognostic factors from recurrence in elderly glioblastoma patients treated by maximal safe resection followed by standard combined chemoradiotherapy as first-line therapy. Multicentric retrospective analysis comparing the prognosis and optimal oncological management of recurrent glioblastomas between 660 adult patients aged of < 70 years (standard group) and 117 patients aged of ≥70 years (elderly group) harboring a supratentorial glioblastoma treated by maximal first-line therapy. From recurrence, both groups did not significantly differ regarding Karnofsky performance status (KPS) (p = 0.482). Oncological treatments from recurrence significantly differed: patients of the elderly group received less frequently oncological treatment from recurrence (p < 0.001), including surgical resection (p < 0.001), Bevacizumab therapy (p < 0.001), and second line chemotherapy other than Temozolomide (p < 0.001). In multivariate analysis, Age ≥70 years was not an independent predictor of overall survival from recurrence (p = 0.602), RTOG-RPA classes 5–6 (p = 0.050) and KPS at recurrence <70 (p < 0.001), available in all cases, were independent significant predictors of shorter overall survival from recurrence. Initial removal of ≥ 90% of enhancing tumor (p = 0.004), initial completion of the standard combined chemoradiotherapy (p = 0.007), oncological treatment from recurrence (p < 0.001), and particularly surgical resection (p < 0.001), Temozolomide (p = 0.046), and Bevacizumab therapy (p = 0.041) were all significant independent predictors of longer overall survival from recurrence. Elderly patients had substandard care from recurrence whereas age did not impact overall survival from recurrence contrary to KPS at recurrence <70. Treatment options from recurrence should include repeat surgery, second line chemotherapy and anti-angiogenic agents.

This article presents an open source software able to convert, display and process medical images. It differentiates itself from the existing software by its ability to design complex processing pipelines and to wisely execute them on a large database. An MP3 pipeline can contain unlimited homemade or ready-made processes and can be carried out with a parallel execution system. As a viewer, MP3 allows to display up to 4 images together and to draw Regions Of Interest (ROI). Two applications showing the strengths of the software are here exposed: a preclinical study involving Magnetic Resonance Imaging (MRI) data and a clinical one involving Computed Tomography (CT) images. MP3 is downloadable at https://github.com/nifm-gin/MP3.

We present a novel 3D reconstruction system that can generate a stable triangle mesh using data from multiple RGB-D sensors in real time for dynamic scenes. The first part of the system uses moving least squares (MLS) point set surfaces to smooth and filter point clouds acquired from RGB-D sensors. The second part of the system generates triangle meshes from point clouds. The whole pipeline is executed on the GPU and is tailored to scale linearly with the size of the input data. Our contributions include changes to the MLS method for improving meshing, a fast triangle mesh generation method and GPU implementations of all parts of the pipeline.

Google Earth (GE) provides very high resolution (VHR) natural-colored (red-green-blue, RGB) images based on commercial spaceborne sensors over worldwide coastal areas. GE is rarely used as a direct data source to address coastal issues despite the tremendous potential of data transferability. This paper describes an inexpensive and easy-to-implement methodology to construct a GE natural-colored dataset with a submeter pixel size over 44 km2 to accurately map the water depth, seabed and land cover along a seamless coastal area in subtropical Japan (Shiraho, Ishigaki Island). The valuation of the GE images for the three mapping types was quantified by comparison with directly-purchased images. We found that both RGB GE-derived mosaic and pansharpened QuickBird (QB) imagery yielded satisfactory results for mapping water depth (R2GE = 0.71 and R2QB = 0.69), seabed cover (OAGE = 89.70% and OAQB = 80.40%, n = 15 classes) and land cover (OAGE = 95.32% and OAQB = 88.71%, n = 11 classes); however, the GE dataset significantly outperformed the QB dataset for all three mappings (ZWater depth = 6.29, ZSeabed = 4.10, ZLand = 3.28, αtwo-tailed < 0.002). The integration of freely available elevation data into both RGB datasets significantly improved the land cover classification accuracy (OAGE = 99.17% and OAQB = 97.80%). Implications and limitations of our findings provide insights for the use of GE VHR data by stakeholders tasked with integrated coastal zone management.

The structure and functioning of coral reef coastal zones are currently coping with an increasing variety of threats, thereby altering the coastal spatial patterns at an accelerated pace. Understanding and predicting the evolution of these highly valuable coastal ecosystems require reliable and frequent mapping and monitoring of both inhabited terrestrial and marine areas at the individual tree and coral colony spatial scale. The very high spatial resolution (VHR) mapping that was recently spearheaded by WorldView-2 (WV2) sensor with 2 m and 0.5 m multispectral (MS) and panchromatic (Pan) bands has the potential to address this burning issue. The objective of this study was to classify nine terrestrial and twelve marine patch classes with respect to spatial resolution enhancement and coast integrity using eight bands of the WV2 sensor on a coastal zone of Moorea Island, French Polynesia. The contribution of the novel WV2 spectral bands towards classification accuracy at 2 m and 0.5 m were tested using traditional and innovative Pan-sharpening techniques. The land and water classes were examined both separately and combinedly. All spectral combinations that were built only with the novel WV2 bands systematically increased the overall classification accuracy of the standard four band classification. The overall best contribution was attributed to the coastal-red edge-near infrared (NIR) 2 combination (Kappagain = 0.0287), which significantly increased the fleshy and encrusting algae (User’s Accuracygain = 18.18%) class. However, the addition of the yellow-NIR2 combination dramatically impacted the hard coral/algae patches class (Producer’s Accuracyloss = −20.88%). Enhancement of the spatial resolution reduced the standard classification accuracy, depending on the Pan-sharpening technique. The proposed composite method (local maximum) provided better overall results than the commonly used sensor method (systematic). However, the sensor technique produced the highest contribution to the hard coral thicket (PAgain = 30.36%) class with the coastal-red edge-NIR2 combination. Partitioning the coast into its terrestrial and aquatic components lowered the overall standard classification accuracy, while strongly enhancing the hard coral bommie class with the coastal-NIR2 combination (UAgain = 40%) and the green-coastal Normalized Difference Ratio (UAgain = 11.06%). VHR spaceborne remote sensing has the potential to gain substantial innovative insights into the evolution of tropical coastal ecosystems from local to regional scales, to predict the influence of anthropogenic and climate changes and to help design optimized management and conservation frameworks.

The motivation of this paper is to provide an overview of the most recent trends and of the future research directions in color image and video processing. Rather than covering all aspects of the domain this survey covers issues related to the most active research areas in the last two years. It presents the most recent trends as well as the state-of-the-art, with a broad survey of the relevant literature, in the main active research areas in color imaging. It also focuses on the most promising research areas in color imaging science. This survey gives an overview about the issues, controversies, and problems of color image science. It focuses on human color vision, perception, and interpretation. It focuses also on acquisition systems, consumer imaging applications, and medical imaging applications. Next it gives a brief overview about the solutions, recommendations, most recent trends, and future trends of color image science. It focuses on color space, appearance models, color difference metrics, and color saliency. It focuses also on color features, color-based object tracking, scene illuminant estimation and color constancy, quality assessment and fidelity assessment, color characterization and calibration of a display device. It focuses on quantization, filtering and enhancement, segmentation, coding and compression, watermarking, and lastly on multispectral color image processing. Lastly, it addresses the research areas which still need addressing and which are the next and future perspectives of color in image and video processing.

Shallow coral reefs threatened by climate change must be spatio-temporally analyzed in terms of their protection of coastal human populations. This study combines Japanese spatio-temporal gradients of population/asset and coral buffering exposure to stress-inducing and stress-mitigating factors so that the socio-economic and ecological (SEE) resilience tied to coral reefscapes can be regionally mapped (1200 km) at a fine resolution (1 arcsec) over a decade (11 years). Fuzzy logic was employed to associated environmental factors based on the related population/asset/coral buffering responses, as found in the literature. Once the factors were weighted according to their resilience contributions, temporally static patterns were evident: (1) a negative correlation occurs between coral buffering resilience and latitude; (2) the least resilient islands are low-lying, deprived of wide reef barriers, and located on the eastern and southern boundaries of the Nansei archipelago; (3) the southwestern-most, middle and northeastern-most islands have the same SEE resilience; and (4) Sekisei Lagoon islands have a very high coral buffering resilience. To overcome uncertainty, future studies should focus on the socio-ecological adaptive capacity, fine-scale ecological processes (such as coral and fish functional groups) and the prediction of the flood risks in the coming decades.