RDNet: Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network in Optical Remote Sensing Images

Bin Wan; Runmin Cong; Xiaofei Zhou; Hao Fang; Yaoqi Sun; Sam Kwong

cs.CVMar 12, 2026

RDNet: Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network in Optical Remote Sensing Images

Authors: Bin Wan, Runmin Cong, Xiaofei Zhou, Hao Fang, Yaoqi Sun, Sam Kwong

Organizations: School of Control Science and Engineering, Shandong University, Jinan 250061, China · Key Laboratory of Machine Intelligence and System Control, Ministry of Education, Jinan 250061, China · School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China · School of Mathematics and Computer Science, Lishui University and Lishui Institute of Hangzhou Dianzi University, Hangzhou 310018, China · School of Data Science, Lingnan University, Tuen Mun, Hong Kong

Abstract

Salient object detection (SOD) in remote sensing images faces significant challenges due to large variations in object sizes, the computational cost of self-attention mechanisms, and the limitations of CNN-based extractors in capturing global context and long-range dependencies. Existing methods that rely on fixed convolution kernels often struggle to adapt to diverse object scales, leading to detail loss or irrelevant feature aggregation. To address these issues, this work aims to enhance robustness to scale variations and achieve precise object localization. We propose the Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network (RDNet), which replaces the CNN backbone with the SwinTransformer for global context modeling and introduces three key modules: (1) the Dynamic Adaptive Detail-aware (DAD) module, which applies varied convolution kernels guided by object region proportions; (2) the Frequency-matching Context Enhancement (FCE) module, which enriches contextual information through wavelet interactions and attention; and (3) the Region Proportion-aware Localization (RPL) module, which employs cross-attention to highlight semantic details and integrates a Proportion Guidance (PG) block to assist the DAD module. By combining these modules, RDNet achieves robustness against scale variations and accurate localization, delivering superior detection performance compared with state-of-the-art methods.

Figures & tables

Fig. 1: Influence of convolution kernels of different sizes.

Fig. 2: The overall architecture of proposed RDNet.

Fig. 3: Architecture of the dynamic adaptive detail-aware module.

Fig. 4: Architecture of the frequency-matching context enhancement module.

Fig. 5: Visual comparison of EORSSD, ORSSD and ORSI-4199 datasets.

Dataset	EORSSD			ORSSD			ORSI-4199
Metric	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$E_{\xi}$ $\uparrow$	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$E_{\xi}$ $\uparrow$	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$E_{\xi}$ $\uparrow$
PoolNet	0.0210	0.4614	0.6853	0.0359	0.6141	0.8142	0.0543	0.7353	0.8550
CSNet	0.0171	0.6284	0.8325	0.0188	0.7574	0.9049	0.0525	0.7151	0.8442
R3Net	0.0171	0.4174	0.6472	0.0401	0.7320	0.8713	0.0403	0.7769	0.8828
DPORTNet	0.0151	0.7377	0.8788	0.0222	0.7861	0.8940	0.0571	0.7514	0.8641
DNTD	0.0114	0.7192	0.8714	0.0219	0.7598	0.8862	0.0426	0.8032	0.9019

TABLE I: Quantitative comparisons with 21 methods on EORSSD, ORSSD and ORSI-4199.

Dataset	EORSSD			ORSSD			ORSI-4199
Metric	$P$ $(M)$	$P$ $(F_{\beta})$	$P$ $(E_{\xi})$	$P$ $(M)$	$P$ $(F_{\beta})$	$P$ $(E_{\xi})$	$P$ $(M)$	$P$ $(F_{\beta})$	$P$ $(E_{\xi})$
HFANet	1.7634 $e-$ 11	4.0579 $e-$ 13	3.012 $e-$ 13	3.8254 $e-$ 13	3.9968 $e-$ 15	2.3426 $e-$ 14	2.6769 $e-$ 15	1.1102 $e-$ 16	3.5782 $e-$ 13
VST	9.0617 $e-$ 11	0	5.5511 $e-$ 16	3.8254 $e-$ 13	0	1.9984 $e-$ 15	2.506 $e-$ 12	0	1.9207 $e-$ 14
MCCNet	1.4353 $e-$ 10	3.6082 $e-$ 14	9.2075 $e-$ 11	1.9524 $e-$ 12	1.7231 $e-$ 13	1.0074 $e-$ 09	2.0111 $e-$ 15	1.1313 $e-$ 13	3.531 $e-$ 09
ADSTNet	6.6842 $e-$ 10	2.2335 $e-$ 10	7.4642 $e-$ 07	4.1102 $e-$ 12	1.0014 $e-$ 12	7.2887 $e-$ 07	1.749 $e-$ 15	2.1378 $e-$ 12	1.7031 $e-$ 08
GeleNet	3.8867 $e-$ 10	1.5702 $e-$ 09	1.6749 $e-$ 08	6.062 $e-$ 11	2.6757 $e-$ 12	1.0895 $e-$ 07	6.7808 $e-$ 09	4.1345 $e-$ 09	7.2416 $e-$ 2

TABLE II: The

t-

test of our method with several compared methods on EORSSD, ORSSD and ORSI-4199

Method	PoolNet	R3Net	PA-KRN	MJRBM	SUCA	ERPNet	EMFINet
Speed(FPS)	80.9	2	16	15.5	24	73.7	25
FLOPs(G)	97.6	47.5	617.7	101.3	56.4	227.4	480.9
Method	CorrNet	ACCoNet	HFANet	VST	MCCNet	HFCNet	ADST
Speed(FPS)	100	59.9	26	23	39.5	38	39.5
FLOPs(G)	21.1	368.8	68.3	23.2	234.2	120.41	27.2
Method	ASTT	GeleNet	Ours

TABLE III: Comparison of the model complexity and the average running speed.

Fig. 6: Illustration of the impact of different module.

Fig. 7: Illustration of the impact of wavelet interaction.

Settings	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$S_{\alpha}$ $\uparrow$
w/o DAD	0.0052	0.8550	0.9273
w/o FCE	0.0061	0.8453	0.9224
w/o RPL	0.0054	0.8561	0.9329
Ours	0.0049	0.8563	0.9327

TABLE IV: Ablation study on different modules.

Settings	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$S_{\alpha}$ $\uparrow$
DAD $\_$ 1	0.0051	0.8494	0.9316
DAD $\_$ 2	0.0051	0.8540	0.9322
DAD $\_$ 3	0.0054	0.8589	0.9314
w/o DO	0.0059	0.8505	0.9283
w/o DE	0.0053	0.8502	0.9256
Ours	0.0049	0.8563	0.9327

TABLE V: Ablation study on DAD module.

Settings	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$S_{\alpha}$ $\uparrow$
w/o WI	0.0059	0.8562	0.9271
w/o FE	0.0057	0.8511	0.9270
Ours	0.0049	0.8563	0.9327

TABLE VI: Ablation study on FCE modules.

Settings	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$S_{\alpha}$ $\uparrow$
ResNet	0.0094	0.7662	0.9127
VGG	0.0134	0.7379	0.8680
PVT	0.0073	0.8194	0.9214
ViT	0.0175	0.5742	0.7832
Ours	0.0049	0.8563	0.9327

TABLE VII: Ablation study on different backbones.

Settings	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$S_{\alpha}$ $\uparrow$
Tradition	0.0056	0.8553	0.9283
Attention	0.0050	0.8560	0.9328
Ours	0.0049	0.8563	0.9327

TABLE VIII: Ablation study on different region proportion estimation methods.

Settings	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$S_{\alpha}$ $\uparrow$
T1	0.0052	0.8553	0.9304
T2	0.0055	0.8550	0.9299
Ours	0.0049	0.8563	0.9327

TABLE IX: Ablation study on different threshold settings.

Settings	$M$ $\downarrow$	$F_{\beta}$ $\uparrow$	$S_{\alpha}$ $\uparrow$
SE	0.0054	0.8525	0.9300
CBAM	0.0053	0.8552	0.9306
Addition	0.0054	0.8561	0.9329
Ours	0.0049	0.8563	0.9327

TABLE X: Ablation study on different feature interaction method.

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Mar 13, 2026J/K move · Enter open · S save

G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images

Bin Wan, Runmin Cong, Xiaofei Zhou, Hao Fang+2

the School of Control Science and Engineering, Shandong University, Jinan 250061, China, and also with State Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai 201210, China · School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China · School of information engineering, Huzhou University, Huzhou 313000, China+1

Salient Object Detection Remote Sensing Multi-Scale Feature Fusion

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Remote sensing images captured from aerial perspectives often exhibit significant scale variations and complex backgrounds, posing challenges for salient object detection (SOD). Existing methods typically extract multi-level features at a single scale using uniform attention mechanisms, leading to suboptimal representations and incomplete detection results. To address these issues, we propose a GeoGran-Aware Hierarchical Feature Fusion Network (G2HFNet) that fully exploits geometric and granular cues in optical remote sensing images. Specifically, G2HFNet adopts Swin Transformer as the backbone to extract multi-level features and integrates three key modules: the multi-scale detail enhancement (MDE) module to handle object scale variations and enrich fine details, the dual-branch geo-gran complementary (DGC) module to jointly capture fine-grained details and positional information in mid-level features, and the deep semantic perception (DSP) module to refine high-level positional cues via self-attention. Additionally, a local-global guidance fusion (LGF) module is introduced to replace traditional convolutions for effective multi-level feature integration. Extensive experiments demonstrate that G2HFNet achieves high-quality saliency maps and significantly improves detection performance in challenging remote sensing scenarios.

Abstract

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