United Domain Cognition Network for Salient Object Detection in Optical Remote Sensing Images

TL;DR

This paper introduces UDCNet, a novel deep learning model that combines frequency and spatial domain features to improve salient object detection in optical remote sensing images, outperforming existing methods.

Contribution

The paper proposes a unified network that integrates global frequency and local spatial features, along with semantic and edge information, for enhanced remote sensing image saliency detection.

Findings

UDCNet outperforms 24 state-of-the-art models.

The frequency-spatial transformer effectively combines global and local features.

The dual-branch decoder produces high-quality salient object predictions.

Abstract

Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objects. However, pixel information represents local attributes, which are often correlated with their surrounding context. Even with strategies expanding the local region, spatial features remain biased towards local characteristics, lacking the ability of global perception. To address this problem, we introduce the Fourier transform that generate global frequency features and achieve an image-size receptive field. To be specific, we propose a novel United Domain Cognition Network (UDCNet) to jointly explore the global-local information in the frequency and spatial domains. Technically, we first design a frequency-spatial domain transformer block that mutually amalgamates the complementary local spatial and global frequency features to strength the capability of initial input features. Furthermore, a dense semantic excavation module is constructed to capture higher-level semantic for guiding the positioning of remote sensing objects. Finally, we devise a dual-branch joint optimization decoder that applies the saliency and edge branches to generate high-quality representations for predicting salient objects. Experimental results demonstrate the superiority of the proposed UDCNet method over 24 state-of-the-art models, through extensive quantitative and qualitative comparisons in three widely-used ORSIs-SOD datasets. The source code is available at: \href{https://github.com/CSYSI/UDCNet}{\color{blue} https://github.com/CSYSI/UDCNet}.