Improved-Flow Warp Module for Remote Sensing Semantic Segmentation

• URL: http://arxiv.org/abs/2205.04160v1
• Date: Mon, 9 May 2022 10:15:18 GMT
• Title: Improved-Flow Warp Module for Remote Sensing Semantic Segmentation
• Authors: Yinjie Zhang, Yi Liu, Wei Guo
• Abstract summary: We propose a new module, called improved-flow warp module (IFWM), to adjust semantic feature maps across different scales for remote sensing semantic segmentation. IFWM computes the offsets of pixels by a learnable way, which can alleviate the misalignment of the multi-scale features. We validate our method on several remote sensing datasets, and the results prove the effectiveness of our method.
• Score: 9.505303195320023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing semantic segmentation aims to assign automatically each pixel on aerial images with specific label. In this letter, we proposed a new module, called improved-flow warp module (IFWM), to adjust semantic feature maps across different scales for remote sensing semantic segmentation. 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. We validate our method on several remote sensing datasets, and the results prove the effectiveness of our method..

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