{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:02:24Z","timestamp":1790071344895,"version":"4.0.1"},"reference-count":73,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Defense Industrial Technology Development Program","award":["JCKY2024530C008"],"award-info":[{"award-number":["JCKY2024530C008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Inverse Synthetic Aperture Radar (ISAR) imagery plays a crucial role in space situational awareness, yet its interpretation remains largely confined to tasks such as classification and segmentation, lacking the ability to generate detailed natural language captions. To address this limitation, this paper proposes ISARCap 1.0, the first dataset specifically designed for ISAR image captioning, covering 38 classes of space targets and comprising 102,965 simulated images and 514,825 image\u2013text pairs. On this basis, this paper proposes ISAR-Mamba, a dual-stream gated state space model for ISAR image captioning. The model introduces a Dual-Stream Asymmetric Encoder (DSAE), which performs complementary patch partitioning and scanning along the range and azimuth dimensions, respectively, to accommodate the physical dimensional differences of ISAR images. Meanwhile, a Sparsity-Aware Gating Mechanism (SAGM) is introduced, which jointly suppresses the interference of empty patches on state updates by leveraging scattering energy priors and a learnable scoring module. Furthermore, a Hierarchical Spatial Summarization (HSS) strategy is proposed to extract structured summary tokens from different depths and spatial regions of the encoder, thereby enhancing visual information perception during text sequence generation. Experimental results on the ISARCap 1.0 dataset indicate that ISAR-Mamba outperforms existing image captioning methods on multiple metrics, suggesting the effectiveness of the proposed method and the usability of the dataset.<\/jats:p>","DOI":"10.3390\/s26185916","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:04:17Z","timestamp":1790067857000},"page":"5916","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ISAR-Mamba: A Dual-Stream Gated Mamba with Hierarchical Spatial Summaries for ISAR Image Captioning"],"prefix":"10.3390","volume":"26","author":[{"given":"Yonghua","family":"He","sequence":"first","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Aoxiang","family":"Pan","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Yonggang","family":"Li","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"}]},{"given":"Jiahao","family":"Wang","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"}]},{"given":"Wei","family":"Qu","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Weigang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Wenhang","family":"Ji","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"}]},{"given":"Guodian","family":"Tang","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Junyi","family":"Lv","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8501","DOI":"10.1109\/TAES.2025.3546184","article-title":"Parametric Curved-Surface Imaging Algorithm for Space Target ISAR Imaging with Rotating Component","volume":"61","author":"Hu","year":"2025","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Z., Duan, J., and Zhang, L. (2025). Explainable Shape Anomaly Detection of Space Targets from ISAR Image Sequences. Remote Sens., 17.","DOI":"10.3390\/rs17213541"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhao, L., Wang, J., Su, J., and Luo, H. (2024). Spatial Feature-Based ISAR Image Registration for Space Targets. Remote Sens., 16.","DOI":"10.3390\/rs16193625"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, J., Ning, X., Sun, D., and Du, R. (2025). An Attitude Estimation Method for Space Targets Based on the Selection of Multi-View ISAR Image Sequences. Remote Sens., 17.","DOI":"10.3390\/rs17203432"},{"key":"ref_5","first-page":"1","article-title":"Few-Shot Multistatic ISAR Target Classification Based on Multilevel Feature Reconstruction","volume":"63","author":"Yang","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"1","article-title":"Complex-Valued Graph Neural Network on Space Target Classification for Defocused ISAR Images","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5494","DOI":"10.1109\/TAES.2022.3174826","article-title":"Robust ISAR Target Recognition Based on ADRISAR-Net","volume":"58","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9129","DOI":"10.1109\/TAES.2024.3438749","article-title":"Feature Distribution Transfer Learning for Robust Few-Shot ISAR Space Target Recognition","volume":"60","author":"Xue","year":"2024","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2025.3609626","article-title":"Scattering Characteristics Guided Network for ISAR Space Target Component Segmentation","volume":"22","author":"Zhong","year":"2025","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3291170","article-title":"ISAR Image Segmentation for Space Target Based on Contrastive Learning and NL-Unet","volume":"20","author":"Kou","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s40537-026-01377-w","article-title":"A comprehensive survey on deep learning approaches for image captioning: A systematic review","volume":"13","author":"Hamdoun","year":"2026","journal-title":"J. Big Data"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"132385","DOI":"10.1016\/j.neucom.2025.132385","article-title":"Pixels to prose: A comprehensive survey of image captioning techniques with deep learning and generative artificial intelligence","volume":"667","author":"Sharma","year":"2026","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100159","DOI":"10.1016\/j.nlp.2025.100159","article-title":"Next-generation image captioning: A survey of methodologies and emerging challenges from transformers to Multimodal Large Language Models","volume":"12","author":"Abdulgalil","year":"2025","journal-title":"Nat. Lang. Process. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2015, January 7\u201312). Show and Tell: A Neural Image Caption Generator. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106112","DOI":"10.1016\/j.engappai.2023.106112","article-title":"Image caption generation using a dual attention mechanism","volume":"123","author":"Padate","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nguyen, V.-Q., Suganuma, M., and Okatani, T. (2022, January 25\u201327). GRIT: Faster and Better Image Captioning Transformer Using Dual Visual Features. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20059-5_10"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7005","DOI":"10.1109\/TCSVT.2022.3178844","article-title":"Vision-Enhanced and Consensus-Aware Transformer for Image Captioning","volume":"32","author":"Cao","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108039","DOI":"10.1016\/j.neunet.2025.108039","article-title":"State-space modeling in long sequence processing: A survey on recurrence in the transformer era","volume":"193","author":"Tiezzi","year":"2026","journal-title":"Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111279","DOI":"10.1016\/j.engappai.2025.111279","article-title":"Mamba-360: Survey of state space models as transformer alternative for long sequence modelling: Methods, Applications, and Challenges","volume":"159","author":"Patro","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_20","unstructured":"Gu, A., and Dao, T. (2024, January 7\u20139). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. Proceedings of the Conference on Language Modeling (COLM), Philadelphia, PA, USA."},{"key":"ref_21","unstructured":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., and Wang, X. (2024, January 21\u201327). Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model. Proceedings of the International Conference on Machine Learning (ICML), Vienna, Austria."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., and Liu, Y. (2024, January 10\u201315). VMamba: Visual State Space Model. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada.","DOI":"10.52202\/079017-3273"},{"key":"ref_23","unstructured":"Huang, T., Pei, X., You, S., Wang, F., Qian, C., and Xu, C. (October, January 29). LocalMamba: Visual State Space Model with Windowed Selective Scan. Proceedings of the European Conference on Computer Vision (ECCV), Milan, Italy."},{"key":"ref_24","unstructured":"Yang, C., Chen, Z., Espinosa, M., Ericsson, L., Wang, Z., Liu, J., and Crowley, E.J. (2024, January 25\u201328). PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition. Proceedings of the British Machine Vision Conference (BMVC), Glasgow, UK."},{"key":"ref_25","first-page":"1","article-title":"Compact Polarimetric ISAR Space Target Components Recognition with Dual-Branch Correlation Aggregation Graph Attention Network","volume":"63","author":"Li","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1109\/TAES.2015.140413","article-title":"ISAR image analysis and recovery with unavailable or heavily corrupted data","volume":"51","author":"Stankovic","year":"2015","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/LGRS.2020.2982563","article-title":"A Novel ISAR Imaging Method Using Pattern-Coupled Laplacian Scale Mixture Prior","volume":"18","author":"Lu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5541116","DOI":"10.1155\/2021\/5541116","article-title":"High-Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm","volume":"2021","author":"Feng","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3208339","article-title":"Ultrawideband ISAR Imaging of Maneuvering Targets with Joint High-Order Motion Compensation and Azimuth Scaling","volume":"60","author":"Shao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1162\/tacl_a_00166","article-title":"From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions","volume":"2","author":"Young","year":"2014","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Janarthanan, A., Paramarthalingam, A., Sundaramurthy, S., and S, Y. (2025, January 1\u20132). Advancements in Image Captioning: A Comprehensive Survey on Techniques, Modalities, and Applications. Proceedings of the International Conference on Decision Aid Sciences and Applications (DASA), Manama, Bahrain.","DOI":"10.1109\/DASA68193.2025.11499090"},{"key":"ref_33","first-page":"e44389-024-01573-w","article-title":"A Review of Image Captioning Techniques: Types, Deep Learning Advancements, and Limitations","volume":"2","author":"Bhosale","year":"2025","journal-title":"Cureus J. Comput. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"13711","DOI":"10.1109\/ACCESS.2024.3356551","article-title":"A Study of ConvNeXt Architectures for Enhanced Image Captioning","volume":"12","author":"Ramos","year":"2024","journal-title":"IEEE Access"},{"key":"ref_35","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015, January 6\u201311). Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., and Zhang, L. (2018, January 18\u201323). Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00636"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4467","DOI":"10.1109\/TCSVT.2019.2947482","article-title":"Multimodal Transformer with Multi-View Visual Representation for Image Captioning","volume":"30","author":"Yu","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s00530-022-01036-z","article-title":"Layer-wise enhanced transformer with multi-modal fusion for image caption","volume":"29","author":"Li","year":"2023","journal-title":"Multimed. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4829","DOI":"10.1109\/TCSVT.2023.3336371","article-title":"SPT: Spatial Pyramid Transformer for Image Captioning","volume":"34","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112135","DOI":"10.1016\/j.engappai.2025.112135","article-title":"Key region Semantic information Augmented Transformer for Image Captioning","volume":"161","author":"Deng","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_41","unstructured":"Wang, J., Yang, Z., Hu, X., Li, L., Lin, K., Gan, Z., Liu, Z., Liu, C., and Wang, L. (2022). GIT: A Generative Image-to-text Transformer for Vision and Language. Trans. Mach. Learn. Res., 2022."},{"key":"ref_42","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning Transferable Visual Models from Natural Language Supervision. Proceedings of the International Conference on Machine Learning (ICML), Virtual."},{"key":"ref_43","unstructured":"Li, J., Li, D., Xiong, C., and Hoi, S. (2022, January 17\u201323). BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. Proceedings of the International Conference on Machine Learning (ICML), Baltimore, MD, USA."},{"key":"ref_44","unstructured":"Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., and Wu, Y. (2022). CoCa: Contrastive Captioners are Image-Text Foundation Models. Trans. Mach. Learn. Res."},{"key":"ref_45","unstructured":"Li, J., Li, D., Savarese, S., and Hoi, S. (2023, January 23\u201329). BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. Proceedings of the International Conference on Machine Learning (ICML), Honolulu, HI, USA."},{"key":"ref_46","unstructured":"Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M. (2024, January 7\u201311). MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, C., Wu, Q., and Lee, Y.J. (2023, January 10\u201316). Visual Instruction Tuning. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA.","DOI":"10.52202\/075280-1516"},{"key":"ref_48","unstructured":"Gu, A., Goel, K., and R\u00e9, C. (2022, January 25\u201329). Efficiently Modeling Long Sequences with Structured State Spaces. Proceedings of the International Conference on Learning Representations (ICLR), Virtual."},{"key":"ref_49","unstructured":"Gupta, A., Gu, A., and Berant, J. (December, January 28). Diagonal State Spaces are as Effective as Structured State Spaces. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA."},{"key":"ref_50","unstructured":"Gu, A., Goel, K., Gupta, A., and R\u00e9, C. (December, January 28). On the Parameterization and Initialization of Diagonal State Space Models. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA."},{"key":"ref_51","unstructured":"Pei, X., Huang, T., and Xu, C. (March, January 25). EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_52","unstructured":"Qiao, Y., Yu, Z., Guo, L., Chen, S., Zhao, Z., Sun, M., Wu, Q., and Liu, J. (2024, January 10\u201315). VL-Mamba: Exploring State Space Models for Multimodal Learning. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_53","unstructured":"Zhao, H., Zhang, M., Zhao, W., Ding, P., Huang, S., and Wang, D. (March, January 25). Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_54","unstructured":"Zou, J., Liao, B., Zhang, Q., Liu, W., and Wang, X. (2025). OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lu, C., Lu, Q., Dong, M., and Luo, J. (2025, January 19\u201325). End-to-End Multi-Modal Diffusion Mamba. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Honolulu, HI, USA.","DOI":"10.1109\/ICCV51701.2025.01909"},{"key":"ref_56","unstructured":"DeepSeek-AI (2026). DeepSeek-v4: Towards Highly Efficient Million-Token Context Intelligence, Hugging Face."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (,  2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA, USA.","DOI":"10.3115\/1073083.1073135"},{"key":"ref_58","unstructured":"Banerjee, S., and Lavie, A. (,  2005). METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Ann Arbor, MI, USA."},{"key":"ref_59","unstructured":"Lin, C.-Y. (2004, January 21\u201326). ROUGE: A Package for Automatic Evaluation of Summaries. Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Barcelona, Spain."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Vedantam, R., Zitnick, C.L., and Parikh, D. (2015, January 7\u201312). CIDEr: Consensus-Based image description evaluation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299087"},{"key":"ref_61","first-page":"1","article-title":"Prior Knowledge-Guided Transformer for Remote Sensing Image Captioning","volume":"61","author":"Meng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","first-page":"1","article-title":"HCNet: Hierarchical Feature Aggregation and Cross-Modal Feature Alignment for Remote Sensing Image Captioning","volume":"62","author":"Yang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","first-page":"1","article-title":"RSIC-GMamba: A State-Space Model with Genetic Operations for Remote Sensing Image Captioning","volume":"63","author":"Meng","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"15361","DOI":"10.1109\/JSTARS.2026.3686075","article-title":"CLIP-Guided Dynamic Feature Fusion with Relation-Enhanced Attention for Remote Sensing Image Captioning","volume":"19","author":"Sima","year":"2026","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lu, J., Xiong, C., Parikh, D., and Socher, R. (2017, January 21\u201326). Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.345"},{"key":"ref_66","unstructured":"Huang, L., Wang, W., Chen, J., and Wei, X.-Y. (November, January 27). Attention on Attention for Image Captioning. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1109\/TMM.2020.2990074","article-title":"CaptionNet: A Tailor-made Recurrent Neural Network for Generating Image Descriptions","volume":"23","author":"Yang","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"10532","DOI":"10.1109\/TGRS.2020.3044054","article-title":"Word\u2013Sentence Framework for Remote Sensing Image Captioning","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4848","DOI":"10.1080\/17538947.2023.2283482","article-title":"MC-Net: Multi-Scale Contextual Information Aggregation Network for Image Captioning on Remote Sensing Images","volume":"16","author":"Huang","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_70","first-page":"1","article-title":"Interactive Concept Network Enhanced Transformer for Remote Sensing Image Captioning","volume":"63","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"100538","DOI":"10.1016\/j.array.2025.100538","article-title":"Mamba-caption: Long-range sequence modelling for efficient and accurate image captioning","volume":"28","author":"Shahzad","year":"2025","journal-title":"Array"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"14870","DOI":"10.1109\/JSTARS.2026.3682864","article-title":"DPPF: Dual-Path Prefusion with Semantic-Guided Encoding for Remote Sensing Image Captioning","volume":"19","author":"Wang","year":"2026","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Han, X., Wu, Z., Li, Y., Zhang, X., Wang, G., and Hou, B. (2026). CSSA: A Cross-Modal Spatial\u2013Semantic Alignment Framework for Remote Sensing Image Captioning. Remote Sens., 18.","DOI":"10.3390\/rs18030522"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5916\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:13:31Z","timestamp":1790068411000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5916"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":73,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185916"],"URL":"https:\/\/doi.org\/10.3390\/s26185916","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]}}}