Better segment aerial photo by learning multi-resolution features with IFWM
Better segment aerial photo by learning multi-resolution features with IFWM
Improved-Flow Warp Module for Remote Sensing Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2205.04160
arXiv PDF paper https://arxiv.org/ftp/arxiv/papers/2205/2205.04160.pdf
Remote sensing semantic segmentation aims to assign automatically each pixel on aerial images with specific label.
... proposed a new module, called improved-flow warp module (IFWM), to adjust semantic feature maps across different scales
... The improved-flow warp module is applied along with the feature extraction process in the convolutional neural network.
First, IFWM computes the offsets of pixels by a learnable way, which can alleviate the misalignment of the multi-scale features.
Second, the offsets help with the low-resolution deep feature up-sampling process to improve the feature accordance, which boosts the accuracy of semantic segmentation.
... validate ... method on several remote sensing datasets, and the results prove the effectiveness of ... method.
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