Dinosaurs’ True Colors

Scientists decode the full-body color patterns of a small-sized dinosaur on March 10, the Beijing Museum of Natural History (BMNH)

Unification and Harmonization in True Color Image City Map

True color image city map is a sort of new-style map which combines the high resolution image and map symbols and shows both advantages in visualization. At the same time, the map unification and harmonization should be taken into account dur-ing the design process, since some visual conflicts appear when map symbols overlaid on the true color image. The objective of this research is to explore the rules in the process of true color image city map design based on chromatic and aesthetic knowledge. At the end, taking the Image Atlas of Guangzhou as an example, image color adjustment, road network presentation, and symbol de-signing issues will be discussed in the application.

Synthesis of True Color Images from the Fengyun Advanced Geostationary Radiation Imager

The production of true color images requires observational data in the red,green,and blue(RGB)bands.The Advanced Geostationary Radiation Imager(AGRI)onboard China’s Fengyun-4(FY-4)series of geostationary satellites only has blue and red bands,and we therefore have to synthesize a green band to produce RGB true color images.We used random forest regression and conditional generative adversarial networks to train the green band model using Himawari-8 Advanced Himawari Imager data.The model was then used to simulate the green channel reflectance of the FY-4 AGRI.A single-scattering radiative transfer model was used to eliminate the contribution of Rayleigh scattering from the atmosphere and a logarithmic enhancement was applied to process the true color image.The conditional generative adversarial network model was better than random forest regression for the green band model in terms of statistical significance(e.g.,a higher determination coefficient,peak signal-to-noise ratio,and structural similarity index).The sharpness of the images was significantly improved after applying a correction for Rayleigh scattering,and the images were able to show natural phenomena more vividly.The AGRI true color images could be used to monitor dust storms,forest fires,typhoons,volcanic eruptions,and other natural events.

A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery

Accurate and timely information on urban vegetation(UV)can be used as an important indicator to estimate the health of cities.Due to the low cost of RGB cameras,true color imagery(TCI)has been widely used for high spatial resolution UV mapping.However,the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples,respectively.To address this issue,an index-guided semantic segmentation(IGSS)framework is proposed in this paper.Firstly,a novel cross-scale vegetation index(CSVI)is calculated by the combination of TCI and Sentinel-2 images,and the index value can be used to provide an initial UV map.Secondly,reliable UV and non-UV samples are automatically generated for training the semantic segmentation model,and then the refined UV map can be produced.The experimental results show that the proposed CSVI outperformed the existingfive RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds,and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72%∼88.16%and an F1 score of 87.73%∼88.37%,which is comparable with the fully-supervised method.