Improved-Flow Warp Module for Remote Sensing Semantic Segmentation

TL;DR

This paper introduces the improved-flow warp module (IFWM), a novel component that enhances multi-scale feature alignment in remote sensing semantic segmentation, leading to improved accuracy in pixel-level classification of aerial images.

Contribution

The paper proposes the IFWM, a learnable module that improves feature alignment across scales in CNNs for remote sensing segmentation, which was not addressed in prior work.

Findings

IFWM improves segmentation accuracy on multiple datasets.

The module effectively alleviates multi-scale feature misalignment.

Enhanced feature up-sampling results in better pixel classification.

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..