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TitleUnsupervised colour coding for visualization of categorical maps
AuthorBeauchemin, M
SourceRemote Sensing Letters vol. 10, no. 1, 2018 p. 77-85,
Alt SeriesNatural Resources Canada, Contribution Series 20180144
PublisherInforma UK Limited
Mediapaper; on-line; digital
File formatpdf (Adobe® Reader®)
Subjectsgeophysics; Science and Technology; remote sensing; computer mapping; cartography; colour; methodology; data processing; visualization; algorithms; classification; contrast
Illustrationsdigital images
ProgramRemote Sensing Science, Methodology
Released2018 11 01
AbstractAn unsupervised algorithm for colour coding of categorical maps is proposed. Our approach is aimed at maximizing colour differentiation between spatially adjacent categories, given a colour palette. The algorithm is specifically designed for maps characterized by the presence of numerous small regions of different categories. The proposed methods relies on a category co-occurrence matrix evaluated locally within non-overlapping and contiguous square blocks of sizes approximately equal to the 2º CIE (Commission Internationale de l'Éclairage) standard observer. The algorithm determine the colour-class association that maximizes the average of the minimum colour contrasts evaluated locally within each block. The algorithm is formulated as an optimization problem on a set of colour permutations. Examples are provided illustrating the performance of the proposed method.
Summary(Plain Language Summary, not published)
Categorical maps are often generated from remote sensing image analysis. For an adequate visual interpretation of these type of maps, each map region need to be colored so that different regions are clearly distinguished between themselves. In this communication, we propose an algorithm designed to automatically assign colors to individual region of a categorical map so that the colour difference between each regions is maximized.