EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion
Salient object detection in remote sensing images (RSI-SOD) aims to identify visually prominent objects by mimicking human visual perception. While convolutional neural networks (CNNs) have significantly improved detection accuracy, most RSI-SOD methods suffer from high computational costs and large...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10980003/ |
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| author | Qian Tang Zhen Wang Xuqi Wang Shan-Wen Zhang |
| author_facet | Qian Tang Zhen Wang Xuqi Wang Shan-Wen Zhang |
| author_sort | Qian Tang |
| collection | DOAJ |
| description | Salient object detection in remote sensing images (RSI-SOD) aims to identify visually prominent objects by mimicking human visual perception. While convolutional neural networks (CNNs) have significantly improved detection accuracy, most RSI-SOD methods suffer from high computational costs and large model sizes, limiting their applicability in resource-constrained environments. Additionally, RSI’s complex backgrounds and diverse object scales further challenge existing methods. To address these issues, we propose EMHANet, a lightweight network that integrates edge texture detail extraction, multi-scale feature fusion, and hybrid attention mechanism. EMHANet consists of MobileNetV3 for feature extraction, an Edge Feature Integration Module (EFIM) for low-level edge details, a Multi-scale Contextual Information Enhancement Module (MCIEM) for high-level feature refinement, and a lightweight decoder for saliency prediction. The network employs a coarse-to-fine strategy to accurately detect salient objects while maintaining efficiency. Experiments on ORSSD and EORSSD datasets demonstrate EMHANet superior performance over 31 state-of-the-art methods. It achieves high accuracy with an inference speed of 143 fps, 0.257M parameters, and 0.92G FLOPs, making it suitable for resource-limited applications. The source code and dataset will be available on <uri>https://github.com/darkseid-arch/EMHANet</uri> |
| format | Article |
| id | doaj-art-084b1b0596dc443b944fc8f58d5ffbef |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-084b1b0596dc443b944fc8f58d5ffbef2025-08-20T03:48:26ZengIEEEIEEE Access2169-35362025-01-0113891648917810.1109/ACCESS.2025.356548510980003EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature FusionQian Tang0Zhen Wang1https://orcid.org/0000-0002-5765-0827Xuqi Wang2Shan-Wen Zhang3https://orcid.org/0000-0002-4534-5358School of Electronic Information, Xijing University, Xi’an, ChinaSchool of Electronic Information, Xijing University, Xi’an, ChinaSchool of Electronic Information, Xijing University, Xi’an, ChinaSchool of Electronic Information, Xijing University, Xi’an, ChinaSalient object detection in remote sensing images (RSI-SOD) aims to identify visually prominent objects by mimicking human visual perception. While convolutional neural networks (CNNs) have significantly improved detection accuracy, most RSI-SOD methods suffer from high computational costs and large model sizes, limiting their applicability in resource-constrained environments. Additionally, RSI’s complex backgrounds and diverse object scales further challenge existing methods. To address these issues, we propose EMHANet, a lightweight network that integrates edge texture detail extraction, multi-scale feature fusion, and hybrid attention mechanism. EMHANet consists of MobileNetV3 for feature extraction, an Edge Feature Integration Module (EFIM) for low-level edge details, a Multi-scale Contextual Information Enhancement Module (MCIEM) for high-level feature refinement, and a lightweight decoder for saliency prediction. The network employs a coarse-to-fine strategy to accurately detect salient objects while maintaining efficiency. Experiments on ORSSD and EORSSD datasets demonstrate EMHANet superior performance over 31 state-of-the-art methods. It achieves high accuracy with an inference speed of 143 fps, 0.257M parameters, and 0.92G FLOPs, making it suitable for resource-limited applications. The source code and dataset will be available on <uri>https://github.com/darkseid-arch/EMHANet</uri>https://ieeexplore.ieee.org/document/10980003/Remote sensingsalient detectionedge featuresmulti-scale fusionattention mechanism |
| spellingShingle | Qian Tang Zhen Wang Xuqi Wang Shan-Wen Zhang EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion IEEE Access Remote sensing salient detection edge features multi-scale fusion attention mechanism |
| title | EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion |
| title_full | EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion |
| title_fullStr | EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion |
| title_full_unstemmed | EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion |
| title_short | EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion |
| title_sort | emhanet lightweight salient object detection for remote sensing images via edge aware multiscale feature fusion |
| topic | Remote sensing salient detection edge features multi-scale fusion attention mechanism |
| url | https://ieeexplore.ieee.org/document/10980003/ |
| work_keys_str_mv | AT qiantang emhanetlightweightsalientobjectdetectionforremotesensingimagesviaedgeawaremultiscalefeaturefusion AT zhenwang emhanetlightweightsalientobjectdetectionforremotesensingimagesviaedgeawaremultiscalefeaturefusion AT xuqiwang emhanetlightweightsalientobjectdetectionforremotesensingimagesviaedgeawaremultiscalefeaturefusion AT shanwenzhang emhanetlightweightsalientobjectdetectionforremotesensingimagesviaedgeawaremultiscalefeaturefusion |