LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery

TL;DR

LOGCAN++ is a specialized semantic segmentation model for remote sensing images that effectively handles background complexity, scale, and orientation variations by combining global and local class-aware modules, outperforming existing methods.

Contribution

The paper introduces LOGCAN++, a novel network with global and local class awareness modules, incorporating affine transformations for adaptive local feature extraction in remote sensing segmentation.

Findings

Outperforms current mainstream segmentation methods on benchmark datasets.

Effectively handles scale and orientation variations in remote sensing images.

Achieves a better trade-off between speed and accuracy.

Abstract

Remote sensing images usually characterized by complex backgrounds, scale and orientation variations, and large intra-class variance. General semantic segmentation methods usually fail to fully investigate the above issues, and thus their performances on remote sensing image segmentation are limited. In this paper, we propose our LOGCAN++, a semantic segmentation model customized for remote sensing images, which is made up of a Global Class Awareness (GCA) module and several Local Class Awareness (LCA) modules. The GCA module captures global representations for class-level context modeling to reduce the interference of background noise. The LCA module generates local class representations as intermediate perceptual elements to indirectly associate pixels with the global class representations, targeting at dealing with the large intra-class variance problem. In particular, we introduce affine transformations in the LCA module for adaptive extraction of local class representations to effectively tolerate scale and orientation variations in remotely sensed images. Extensive experiments on three benchmark datasets show that our LOGCAN++ outperforms current mainstream general and remote sensing semantic segmentation methods and achieves a better trade-off between speed and accuracy. Code is available at https://github.com/xwmaxwma/rssegmentation.