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Semantic segmentation of multichannel images is a fundamental task for manyapplications. Selecting an appropriate channel combination from the originalmultichannel image can improve the accuracy of semantic segmentation and reducethe cost of data storage, processing and future acquisition. Existing channelselection methods typically use a reasonable selection procedure to determine adesirable channel combination, and then train a semantic segmentation networkusing that combination. In this study, the concept of pruning from a supernetis used for the first time to integrate the selection of channel combinationand the training of a semantic segmentation network. Based on this concept, aOne-Shot Task-Adaptive (OSTA) channel selection method is proposed for thesemantic segmentation of multichannel images. OSTA has three stages, namely thesupernet training stage, the pruning stage and the fine-tuning stage. Theoutcomes of six groups of experiments (L7Irish3C, L7Irish2C, L8Biome3C,L8Biome2C, RIT-18 and Semantic3D) demonstrated the effectiveness and efficiencyof OSTA. OSTA achieved the highest segmentation accuracies in all tests (62.49%(mIoU), 75.40% (mIoU), 68.38% (mIoU), 87.63% (mIoU), 66.53% (mA) and 70.86%(mIoU), respectively). It even exceeded the highest accuracies of exhaustivetests (61.54% (mIoU), 74.91% (mIoU), 67.94% (mIoU), 87.32% (mIoU), 65.32% (mA)and 70.27% (mIoU), respectively), where all possible channel combinations weretested. All of this can be accomplished within a predictable and relativelyefficient timeframe, ranging from 101.71% to 298.1% times the time required totrain the segmentation network alone. In addition, there were interestingfindings that were deemed valuable for several fields.

OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images