# Boundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery

arXiv: https://arxiv.org/abs/2303.02867 · HTML: https://mlsanity.com/paper/2303.02867/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 88 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Visual Saliency Based on Multiscale Deep Features](https://mlsanity.com/paper/1503.08663/) (2015)
- [Suppress and Balance: A Simple Gated Network for Salient Object Detection](https://mlsanity.com/paper/2007.08074/) (2020)
- [Salient Object Detection: A Discriminative Regional Feature Integration Approach](https://mlsanity.com/paper/1410.5926/) (2014)
- [Boundary-Guided Camouflaged Object Detection](https://mlsanity.com/paper/2207.00794/) (2022)
- [EDN: Salient Object Detection via Extremely-Downsampled Network](https://mlsanity.com/paper/2012.13093/) (2020)
- [Pyramid Grafting Network for One-Stage High Resolution Saliency Detection](https://mlsanity.com/paper/2204.05041/) (2022)
- [Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2011.13144/) (2020)
- [Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/1906.08462/) (2019)
- [RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2110.14223/) (2021)
- [Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network](https://mlsanity.com/paper/1502.06796/) (2015)
- [Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps](https://mlsanity.com/paper/1802.07957/) (2018)
- [Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment](https://mlsanity.com/paper/2301.02778/) (2023)
- [Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation](https://mlsanity.com/paper/2201.08049/) (2022)
- [Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation](https://mlsanity.com/paper/2101.09014/) (2021)

## Built on by (most cited first, 4 of 4)

- [Localization, balance and affinity: a stronger multifaceted collaborative salient object detector in remote sensing images](https://mlsanity.com/paper/2410.23991/) (2024)
- [Teaching in adverse scenes: a statistically feedback-driven threshold and mask adjustment teacher-student framework for object detection in UAV images under adverse scenes](https://mlsanity.com/paper/2506.11175/) (2025)
- [Beyond Global Scanning: Adaptive Visual State Space Modeling for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2508.10542/) (2025)
- [SPLG-Mamba: Structure-Preserving Local-Global Mamba Network for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2608.29626/) (2026)

## Abstract (arXiv)

With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth, but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps. To address these issues, we propose a novel method named boundary-semantic collaborative guidance network (BSCGNet) with dual-stream feedback mechanism. First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary ground truth. Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication. Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism. Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years. Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.