OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation
  of Multichannel Images
Yuanzhi Cai Jagannath Aryal Yuan Fang Hong Huang Lei Fan
Semantic segmentation of multichannel images is a fundamental task for many
applications. Selecting an appropriate channel combination from the original
multichannel image can improve the accuracy of semantic segmentation and reduce
the cost of data storage, processing and future acquisition. Existing channel
selection methods typically use a reasonable selection procedure to determine a
desirable channel combination, and then train a semantic segmentation network
using that combination. In this study, the concept of pruning from a supernet
is used for the first time to integrate the selection of channel combination
and the training of a semantic segmentation network. Based on this concept, a
One-Shot Task-Adaptive (OSTA) channel selection method is proposed for the
semantic segmentation of multichannel images. OSTA has three stages, namely the
supernet training stage, the pruning stage and the fine-tuning stage. The
outcomes of six groups of experiments (L7Irish3C, L7Irish2C, L8Biome3C,
L8Biome2C, RIT-18 and Semantic3D) demonstrated the effectiveness and efficiency
of 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 exhaustive
tests (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 were
tested. All of this can be accomplished within a predictable and relatively
efficient timeframe, ranging from 101.71% to 298.1% times the time required to
train the segmentation network alone. In addition, there were interesting
findings that were deemed valuable for several fields.