{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T19:48:06Z","timestamp":1767642486804,"version":"3.48.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032156204","type":"print"},{"value":"9783032156211","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-15621-1_27","type":"book-chapter","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T16:38:16Z","timestamp":1767631096000},"page":"326-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Small-Object Detection in\u00a0Satellite Imagery Using Dual Attention-Augmented Single-Stage CNN Model"],"prefix":"10.1007","author":[{"given":"Likhit","family":"Yammanuru","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikhil Tom","family":"Jose","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rimjhim Padam","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"27_CR1","unstructured":"Liu, Y., Shao, Z., Hoffmann, N.: Global attention mechanism: retain information to enhance channel-spatial interactions. CoRR, abs\/2112.05561 (2021)"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Vinh, T.Q., Long, P.H.: Pedestrian detection using yolo with improved attention module. In: 2023 International Conference on Advanced Computing and Analytics (ACOMPA), pp. 93\u201398 (2023)","DOI":"10.1109\/ACOMPA61072.2023.00024"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: An image fusion method based on special residual network and efficient channel attention. Electronics 11(19) (2022)","DOI":"10.3390\/electronics11193140"},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","volume":"8","author":"S Wei","year":"2020","unstructured":"Wei, S., et al.: HRSID: a high-resolution SAR images dataset for ship detection and instance segmentation. IEEE Access 8, 120234\u2013120254 (2020)","journal-title":"IEEE Access"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: Multi-scale adversarial network for vehicle detection in UAV imagery. ISPRS J. Photogrammetry Remote Sens. 180, 283\u2013295 (2021)","DOI":"10.1016\/j.isprsjprs.2021.08.002"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Wu, J., Pan, Z., Lei, B., Hu, Y.: LR-TSDet: towards tiny ship detection in low-resolution remote sensing images. Remote Sens. 13, 3890 (2021)","DOI":"10.3390\/rs13193890"},{"issue":"2","key":"27_CR7","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1109\/TGRS.2020.2999082","volume":"59","author":"H Wei","year":"2021","unstructured":"Wei, H., et al.: X-Linenet: detecting aircraft in remote sensing images by a pair of intersecting line segments. IEEE Trans. Geosci. Remote Sens. 59(2), 1645\u20131659 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"27_CR8","first-page":"1","volume":"60","author":"Y Han","year":"2022","unstructured":"Han, Y., Yang, X., Tian, P., Peng, Z.: Fine-grained recognition for oriented ship against complex scenes in optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201318 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"27_CR9","first-page":"1","volume":"60","author":"W Jixiang","year":"2022","unstructured":"Jixiang, W., Pan, Z., Lei, B., Yuxin, H.: Fsanet: feature-and-spatial-aligned network for tiny object detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201317 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Yu, R., Li, H., Jiang, Y., Zhang, B., Wang, Y.: Tiny vehicle detection for mid-to-high altitude UAV images based on visual attention and spatial-temporal information. Sensors 22, 2354 (2022)","DOI":"10.3390\/s22062354"},{"key":"27_CR11","first-page":"07","volume":"15","author":"Yu Chaoran","year":"2023","unstructured":"Chaoran, Yu., et al.: HB-YOLO: an improved yolov7 algorithm for dim-object tracking in satellite remote sensing videos. Remote Sens. 15, 07 (2023)","journal-title":"Remote Sens."},{"key":"27_CR12","doi-asserted-by":"publisher","first-page":"6601","DOI":"10.1109\/JSTARS.2024.3373231","volume":"17","author":"C Li","year":"2024","unstructured":"Li, C.: An efficient method for detecting dense and small objects in UAV images. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 17, 6601\u20136615 (2024)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Vandeghen, R., Louppe, G., Van\u00a0Droogenbroeck, M.: Adaptive self-training for object detection. In: 2023 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 914\u2013923 (2023)","DOI":"10.1109\/ICCVW60793.2023.00098"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Etnubarhi, N., Pirehen, H., Nagpal, S., Bayraktar, I.: Assessing yolov8 performance and limitations in aircraft and ship detection from satellite images, pp. 1\u20135 (2024)","DOI":"10.1109\/ASYU62119.2024.10757155"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Ayush, K., Uzkent, B., Burke, M., Lobell, D., Ermon, S.: Generating interpretable poverty maps using object detection in satellite images, pp. 4367\u20134373 (2020)","DOI":"10.24963\/ijcai.2020\/608"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Biswas, D., Te\u0161i\u0107, J.: Small object difficulty (sod) modeling for objects detection in satellite images. In: 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 125\u2013130 (2022)","DOI":"10.1109\/CICN56167.2022.10008383"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Van\u00a0Etten, A., et al.: The multi-temporal urban development spacenet dataset. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6394\u20136403 (2021)","DOI":"10.1109\/CVPR46437.2021.00633"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Boguszewski, A., Batorski, D., Jankowska, N.Z., Zambrzycka, A., Dziedzic, T.: Landcover.ai: dataset for automatic mapping of buildings, woodlands and water from aerial imagery (2020)","DOI":"10.1109\/CVPRW53098.2021.00121"},{"key":"27_CR19","unstructured":"Rizk, M., Dominique, H., Baghdadi, A., Diguet, J.: Marine object detection based on top-view scenes using deep learning on edge devices (2022)"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Gupta, R., Shah, M.: Rescuenet: joint building segmentation and damage assessment from satellite imagery. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 4405\u20134411 (2021)","DOI":"10.1109\/ICPR48806.2021.9412295"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Pang, Y., Cheng, S., Hu, J., Liu, Y.: Robust satellite image classification with Bayesian deep learning. In: 2022 Integrated Communication, Navigation and Surveillance Conference (ICNS), pp. 1\u20138 (2022)","DOI":"10.1109\/ICNS54818.2022.9771496"},{"key":"27_CR22","first-page":"1","volume":"62","author":"B Liu","year":"2024","unstructured":"Liu, B., Chen, S.B., Wang, J.X., Tang, J., Luo, B.: An oriented object detector for hazy remote sensing images. IEEE Trans. Geosci. Remote Sens. 62, 1\u201311 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zorzi, S., Bittner, K.: Machine-learned 3D building vectorization from satellite imagery (2021)","DOI":"10.1109\/CVPRW53098.2021.00118"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Martinson, E., Furlong, B., Gillies, A.: Training rare object detection in satellite imagery with synthetic GAN images. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2763\u20132770 (2021)","DOI":"10.1109\/CVPRW53098.2021.00311"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Hassan, I., Xinyou, Z.: Performance evolution of yolo models in remote sensing images. In: 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 1\u20134 (2024)","DOI":"10.1109\/ICCWAMTIP64812.2024.10873654"}],"container-title":["Lecture Notes in Computer Science","Applied Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15621-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T16:38:25Z","timestamp":1767631105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15621-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032156204","9783032156211"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15621-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"6 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Algorithms","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 January 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 January 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaa2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icaa2026.framer.website\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}