RSRefSeg: Referring Remote Sensing Image Segmentation with Foundation Models

TL;DR

RSRefSeg introduces a foundation model for referring remote sensing image segmentation that combines CLIP and SAM to improve fine-grained visual understanding and segmentation accuracy in remote sensing applications.

Contribution

It presents a novel framework that leverages CLIP and SAM for better multimodal alignment and segmentation in remote sensing images, addressing limitations of previous methods.

Findings

Outperforms existing methods on RRSIS-D dataset

Effectively aligns fine-grained semantic concepts across modalities

Enhances segmentation accuracy in remote sensing images

Abstract

Referring remote sensing image segmentation is crucial for achieving fine-grained visual understanding through free-format textual input, enabling enhanced scene and object extraction in remote sensing applications. Current research primarily utilizes pre-trained language models to encode textual descriptions and align them with visual modalities, thereby facilitating the expression of relevant visual features. However, these approaches often struggle to establish robust alignments between fine-grained semantic concepts, leading to inconsistent representations across textual and visual information. To address these limitations, we introduce a referring remote sensing image segmentation foundational model, RSRefSeg. RSRefSeg leverages CLIP for visual and textual encoding, employing both global and local textual semantics as filters to generate referring-related visual activation features in the latent space. These activated features then serve as input prompts for SAM, which refines the segmentation masks through its robust visual generalization capabilities. Experimental results on the RRSIS-D dataset demonstrate that RSRefSeg outperforms existing methods, underscoring the effectiveness of foundational models in enhancing multimodal task comprehension. The code is available at \url{https://github.com/KyanChen/RSRefSeg}.