Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation

TL;DR

This paper introduces PDSSNet, a novel network for remote sensing image segmentation that effectively combines class semantics and spatial structure to improve accuracy, addressing intra-class variance and inter-class similarity challenges.

Contribution

The paper proposes a prototype-driven network with three modules that jointly model class semantics and spatial structure, advancing segmentation performance in remote sensing images.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Achieves higher segmentation accuracy and structural integrity

Demonstrates robustness to intra-class variance and inter-class similarity

Abstract

In the semantic segmentation of remote sensing images, acquiring complete ground objects is critical for achieving precise analysis. However, this task is severely hindered by two major challenges: high intra-class variance and high inter-class similarity. Traditional methods often yield incomplete segmentation results due to their inability to effectively unify class representations and distinguish between similar features. Even emerging class-guided approaches are limited by coarse class prototype representations and a neglect of target structural information. Therefore, this paper proposes a Prototype-Driven Structure Synergy Network (PDSSNet). The design of this network is based on a core concept, a complete ground object is jointly defined by its invariant class semantics and its variant spatial structure. To implement this, we have designed three key modules. First, the Adaptive Prototype Extraction Module (APEM) ensures semantic accuracy from the source by encoding the ground truth to extract unbiased class prototypes. Subsequently, the designed Semantic-Structure Coordination Module (SSCM) follows a hierarchical semantics-first, structure-second principle. This involves first establishing a global semantic cognition, then leveraging structural information to constrain and refine the semantic representation, thereby ensuring the integrity of class information. Finally, the Channel Similarity Adjustment Module (CSAM) employs a dynamic step-size adjustment mechanism to focus on discriminative features between classes. Extensive experiments demonstrate that PDSSNet outperforms state-of-the-art methods. The source code is available at https://github.com/wangjunyi-1/PDSSNet.