Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images

TL;DR

This paper introduces MCCNet, a novel neural network architecture that leverages multiple content types and attention mechanisms to improve salient object detection in optical remote sensing images, outperforming existing methods.

Contribution

The paper proposes MCCNet with a unique Multi-Content Complementation Module that effectively exploits feature complementarity for RSI-SOD, a novel approach in this domain.

Findings

Outperforms 23 state-of-the-art methods on two datasets

Effectively utilizes multiple feature types for better detection

Demonstrates robustness across different scales and conditions

Abstract

In the computer vision community, great progresses have been achieved in salient object detection from natural scene images (NSI-SOD); by contrast, salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. The unique characteristics of optical RSIs, such as scales, illuminations and imaging orientations, bring significant differences between NSI-SOD and RSI-SOD. In this paper, we propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. Specifically, MCCNet is based on the general encoder-decoder architecture, and contains a novel key component named Multi-Content Complementation Module (MCCM), which bridges the encoder and the decoder. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features, and exploit the content complementarity between them to highlight salient regions over various scales in RSI features through the attention mechanism. Besides, we comprehensively introduce pixel-level, map-level and metric-aware losses in the training phase. Extensive experiments on two popular datasets demonstrate that the proposed MCCNet outperforms 23 state-of-the-art methods, including both NSI-SOD and RSI-SOD methods. The code and results of our method are available at https://github.com/MathLee/MCCNet.