{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T15:39:52Z","timestamp":1778341192326,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Object detection in remote sensing imagery is critical in environmental monitoring, urban planning, and land resource management. However, the task remains challenging due to significant scale variations, arbitrary object orientations, and complex background clutter. To address these issues, we propose a novel orientation module (SOAM Block) that jointly models object scale and directional features while exploiting geometric symmetry inherent in many remote sensing targets. The SOAM Block is constructed upon a lightweight and efficient Adaptive Multi-Scale (AMS) Module, which utilizes a symmetric arrangement of parallel depth-wise convolutional branches with varied kernel sizes to extract fine-grained multi-scale features without dilation, thereby preserving local context and enhancing scale adaptability. In addition, a Strip-based Context Attention (SCA) mechanism is introduced to model long-range spatial dependencies, leveraging horizontal and vertical 1D strip convolutions in a directionally symmetric fashion. This design captures spatial correlations between distant regions and reinforces semantic consistency in cluttered scenes. Importantly, this work is the first to explicitly analyze the coupling between object scale and orientation in remote sensing imagery. The proposed method addresses the limitations of fixed receptive fields in capturing symmetric directional cues of large-scale objects. Extensive experiments are conducted on two widely used benchmarks\u2014DOTA and HRSC2016\u2014both of which exhibit significant scale variations and orientation diversity. Results demonstrate that our approach achieves superior detection accuracy with fewer parameters and lower computational overhead compared to state-of-the-art methods. The proposed SOAM Block thus offers a robust, scalable, and symmetry-aware solution for high-precision object detection in complex aerial scenes.<\/jats:p>","DOI":"10.3390\/sym17081251","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T10:13:51Z","timestamp":1754475231000},"page":"1251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SOAM Block: A Scale\u2013Orientation-Aware Module for Efficient Object Detection in Remote Sensing Imagery"],"prefix":"10.3390","volume":"17","author":[{"given":"Yi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710600, China"},{"name":"Xi\u2019an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhidong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Xiong","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinqi","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Li, G. (2025). UAV Imagery Real-Time Semantic Segmentation with Global\u2013Local Information Attention. Sensors, 25.","DOI":"10.3390\/s25061786"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). SCRDet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019, January 15\u201320). Learning roi transformer for oriented object detection in aerial images. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G.S. (2021). ReDet: A Rotation-equivariant Detector for Aerial Object Detection. arXiv.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_5","unstructured":"Yang, X., Yan, J., Feng, Z., and He, T. (2019). R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Guo, Z., Liu, C., Zhang, X., Jiao, J., Ji, X., and Ye, Q. (2021, January 20\u201325). Beyond bounding-box: Convex-hull feature adaptation for oriented and densely packed object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00868"},{"key":"ref_7","unstructured":"Xu, J., and He, X. (2024). DAF-Net: A Dual-Branch Feature Decomposition Fusion Network with Domain Adaptive for Infrared and Visible Image Fusion. arXiv."},{"key":"ref_8","first-page":"5608614","article-title":"Rotation equivariant feature image pyramid network for object detection in optical remote sensing imagery","volume":"60","author":"Shamsolmoali","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","unstructured":"Chen, Y., Zhang, P., Li, Z., Li, Y., Zhang, X., Meng, G., Xiang, S., Sun, J., and Jia, J. (2020). Stitcher: Feedback-driven data provider for object detection. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1109\/TGRS.2019.2900302","article-title":"Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1109\/TCSVT.2019.2905881","article-title":"Small object detection in unmanned aerial vehicle images using feature fusion and scaling-based single shot detector with spatial context analysis","volume":"30","author":"Liang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_13","first-page":"5602918","article-title":"FSoD-Net: Full-scale object detection from optical remote sensing imagery","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"5602511","article-title":"Align deep features for oriented object detection","volume":"60","author":"Han","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"5605414","article-title":"AR2Det: An Accurate and Real-Time Rotational One-Stage Ship Detector in Remote Sensing Images","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5623616","DOI":"10.1109\/TGRS.2022.3173610","article-title":"Ship Detection in High-Resolution Optical Remote Sensing Images Aided by Saliency Information","volume":"60","author":"Ren","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M., Yang, J., and Li, X. (2023, January 2\u20133). Large Selective Kernel Network for Remote Sensing Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yuan, L., Weng, L., and Yang, Y. (2017, January 24\u201326). A high resolution optical satellite image dataset for ship recognition and some new baselines. Proceedings of the International Conference on Pattern Recognition Applications and Methods, Porto, Portugal.","DOI":"10.5220\/0006120603240331"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cai, X., Lai, Q., Wang, Y., Wang, W., Sun, Z., and Yao, Y. (2024, January 16\u201322). Poly Kernel Inception Network for Remote Sensing Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02617"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Darabi, N., Tayebati, S., Ravi, S., Tulabandhula, T., and Trivedi, A.R. (2023). Starnet: Sensor trustworthiness and anomaly recognition via approximated likelihood regret for robust edge autonomy. arXiv.","DOI":"10.1109\/IJCNN60899.2024.10650213"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Khan, S.D., and Basalamah, S. (2023). Multi-branch deep learning framework for land scene classification in satellite imagery. Remote Sens., 15.","DOI":"10.3390\/rs15133408"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, X., Kang, M., Chen, Y., Jiang, W., Wang, M., Weise, T., Tan, M., Xu, L., Li, X., and Zou, L. (2023). Adaptive local cross-channel vector pooling attention module for semantic segmentation of remote sensing imagery. Remote Sens., 15.","DOI":"10.3390\/rs15081980"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"109761","DOI":"10.1016\/j.patcog.2023.109761","article-title":"Learning discriminative feature representation with pixel-level supervision for forest smoke recognition","volume":"143","author":"Tao","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guo, N., Jiang, M., Gao, L., Li, K., Zheng, F., Chen, X., and Wang, M. (2023). HFCC-Net: A dual-branch hybrid framework of CNN and CapsNet for land-use scene classification. Remote Sens., 15.","DOI":"10.3390\/rs15205044"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4700215","DOI":"10.1109\/TGRS.2023.3235886","article-title":"Easy-to-hard structure for remote sensing scene classification in multitarget domain adaptation","volume":"61","author":"Ngo","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"5609311","article-title":"Generalized scene classification from small-scale datasets with multitask learning","volume":"60","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zeng, D., Chen, S., Chen, B., and Li, S. (2018). Improving remote sensing scene classification by integrating global-context and local-object features. Remote Sens., 10.","DOI":"10.3390\/rs10050734"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8160","DOI":"10.1109\/JSTARS.2021.3103744","article-title":"Dual graph U-Nets for hyperspectral image classification","volume":"14","author":"Guo","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.neucom.2019.11.068","article-title":"RADC-Net: A residual attention based convolution network for aerial scene classification","volume":"377","author":"Bi","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, H., and Xu, K. (2019). Combing triple-part features of convolutional neural networks for scene classification in remote sensing. Remote Sens., 11.","DOI":"10.3390\/rs11141687"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wan, D., Lu, R., Wang, S., Shen, S., Xu, T., and Lang, X. (2023). Yolo-hr: Improved yolov5 for object detection in high-resolution optical remote sensing images. Remote Sens., 15.","DOI":"10.3390\/rs15030614"},{"key":"ref_32","first-page":"103975","article-title":"Advanced ship detection and ocean monitoring with satellite imagery and deep learning for marine science applications","volume":"81","author":"Bakirci","year":"2025","journal-title":"Reg. Stud. Mar. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xia, G., Bai, X., Ding, J., Zhu, Z., Belongie, S.J., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201322). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_34","unstructured":"Yang, Z., Liu, S., Li, Z., Shi, Z., Shen, C., and Shao, J. (2016). HRSC2016: A Benchmark for Ship Detection in Aerial Images. Proceedings of the International Conference on Image and Graphics (ICIG), Springer."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., and Jiao, J. (2015, January 27\u201330). Orientation robust object detection in aerial images using deep convolutional neural network. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351502"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1251\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:24:20Z","timestamp":1760034260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":35,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["sym17081251"],"URL":"https:\/\/doi.org\/10.3390\/sym17081251","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,6]]}}}