# MCFNet: Multi-Scale Contextual Fusion Network for Salient Object Detection in Optical Remote Sensing Images

## TL;DR

This paper introduces MCFNet, a new deep learning network for detecting salient objects in optical remote sensing images, improving accuracy and boundary delineation.

## Contribution

The novel MCFNet combines a Semantic-Aware Attention Module and Contextual Interconnection Module for enhanced salient object detection in remote sensing.

## Key findings

- MCFNet outperforms existing methods on standard ORSI-SOD benchmark datasets.
- The network achieves robust and efficient performance in challenging remote sensing scenarios.
- MCFNet improves localization accuracy and boundary delineation for complex and varying-scale objects.

## Abstract

The rapid advancement of deep learning has catalyzed progress in salient object detection (SOD), extending its impact to the domain of optical remote sensing images (ORSIs). Despite increasing attention, salient object detection for optical remote sensing images (ORSI-SOD) remains highly challenging due to the intrinsic complexities of remote sensing scenes. In particular, severe variations in object scale and quantity, cluttered backgrounds, and irregular object morphologies significantly hinder accurate target localization and boundary delineation. In response to these challenges, we introduce the Multi-scale Contextual Fusion Network (MCFNet) for ORSI-SOD. MCFNet incorporates a Semantic-Aware Attention Module (SAM), which provides explicit semantic guidance during feature extraction. By producing preliminary semantic masks, SAM enables the network to capture long-range contextual dependencies, thereby enhancing localization accuracy for salient objects exhibiting substantial scale variation and structural complexity. In addition, MCFNet integrates a Contextual Interconnection Module (CIM), which promotes effective fusion of local and global contextual features. By facilitating cross-layer interactions and adopting a multiscale refinement strategy, CIM enriches texture representations while suppressing background interference, leading to smoother object boundaries and more precise delineation of salient regions. Extensive evaluations conducted on three standard ORSI-SOD benchmark datasets demonstrate the superior performance of MCFNet compared to existing methods, highlighting its robustness and efficiency in handling challenging remote sensing scenarios.