Localization, balance and affinity: a stronger multifaceted collaborative salient object detector in remote sensing images

TL;DR

This paper introduces LBA-MCNet, a novel remote sensing salient object detection model that enhances boundary localization, feature balancing, and global context modeling, outperforming 28 state-of-the-art methods on multiple datasets.

Contribution

The paper proposes a multifaceted collaborative detector with modules for edge localization and global affinity learning, addressing existing challenges in remote sensing image analysis.

Findings

Outperforms 28 state-of-the-art methods

Achieves superior boundary localization and feature balance

Demonstrates effectiveness across multiple datasets

Abstract

Despite significant advancements in salient object detection(SOD) in optical remote sensing images(ORSI), challenges persist due to the intricate edge structures of ORSIs and the complexity of their contextual relationships. Current deep learning approaches encounter difficulties in accurately identifying boundary features and lack efficiency in collaboratively modeling the foreground and background by leveraging contextual features. To address these challenges, we propose a stronger multifaceted collaborative salient object detector in ORSIs, termed LBA-MCNet, which incorporates aspects of localization, balance, and affinity. The network focuses on accurately locating targets, balancing detailed features, and modeling image-level global context information. Specifically, we design the Edge Feature Adaptive Balancing and Adjusting(EFABA) module for precise edge localization, using edge features to guide attention to boundaries and preserve spatial details. Moreover, we design the Global Distributed Affinity Learning(GDAL) module to model global context. It captures global context by generating an affinity map from the encoders final layer, ensuring effective modeling of global patterns. Additionally, deep supervision during deconvolution further enhances feature representation. Finally, we compared with 28 state of the art approaches on three publicly available datasets. The results clearly demonstrate the superiority of our method.