{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:42:30Z","timestamp":1768995750435,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556274","type":"print"},{"value":"9789819556281","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\/978-981-95-5628-1_37","type":"book-chapter","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:30:18Z","timestamp":1768944618000},"page":"538-552","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-Precision Remote Sensing Image Change Detection Based on\u00a0Image Style Unification and\u00a0Feature Extraction Optimization"],"prefix":"10.1007","author":[{"given":"Jinmiao","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zelin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunpeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"issue":"5","key":"37_CR1","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2010","unstructured":"Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. TPAMI 33(5), 898\u2013916 (2010)","journal-title":"TPAMI"},{"issue":"3","key":"37_CR2","first-page":"1171","volume":"38","author":"L Bruzzone","year":"2000","unstructured":"Bruzzone, L., Prieto, D.F.: Automatic analysis of the difference image for unsupervised change detection. TGRS 38(3), 1171\u20131182 (2000)","journal-title":"TGRS"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE visual communications and image processing (VCIP), pp.\u00a01\u20134. IEEE (2017)","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"37_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","volume":"60","author":"H Chen","year":"2021","unstructured":"Chen, H., Qi, Z., Shi, Z.: Remote sensing image change detection with transformers. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"issue":"16","key":"37_CR6","doi-asserted-by":"publisher","first-page":"4823","DOI":"10.1080\/01431160801950162","volume":"29","author":"J Deng","year":"2008","unstructured":"Deng, J., Wang, K., Deng, Y., Qi, G.: PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int. J. Remote Sens. 29(16), 4823\u20134838 (2008)","journal-title":"Int. J. Remote Sens."},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Ding, L., et al.: Joint spatio-temporal modeling for semantic change detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3362795"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Jiang, Z., et al.: Feature enhancement and feedback network for change detection in remote sensing images. IEEE Geosci. Remote Sens. Lett. (2025)","DOI":"10.1109\/LGRS.2025.3536164"},{"key":"37_CR11","unstructured":"Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation. arXiv preprint arXiv:1805.10180 (2018)"},{"key":"37_CR12","first-page":"1","volume":"62","author":"Z Li","year":"2024","unstructured":"Li, Z., et al.: STADE-CDNet: spatial-temporal attention with difference enhancement-based network for remote sensing image change detection. TGRS 62, 1\u201317 (2024)","journal-title":"TGRS"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"37_CR14","first-page":"1","volume":"20","author":"Z Lv","year":"2023","unstructured":"Lv, Z.: Novel enhanced UNet for change detection using multimodal remote sensing image. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"6","key":"37_CR15","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1080\/01431168908903937","volume":"10","author":"N Quarmby","year":"1989","unstructured":"Quarmby, N., Cushnie, J.: Monitoring urban land cover changes at the urban fringe from spot HRV imagery in south-east England. Int. J. Remote Sens. 10(6), 953\u2013963 (1989)","journal-title":"Int. J. Remote Sens."},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Doll\u00e1r, P.: Designing network design spaces. In: CVPR, pp. 10428\u201310436 (2020)","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"37_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI, vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\u00a01\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"37_CR22","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: ICML, pp. 6105\u20136114. PMLR (2019)"},{"key":"37_CR23","first-page":"1","volume":"62","author":"W Wang","year":"2024","unstructured":"Wang, W., Liu, C., Liu, G., Wang, X.: CF-GCN: Graph Convolutional Network for change detection in remote sensing images. TGRS 62, 1\u201313 (2024)","journal-title":"TGRS"},{"key":"37_CR24","first-page":"4861","volume":"15","author":"C Yu","year":"2022","unstructured":"Yu, C.: Precise and fast segmentation of offshore farms in high-resolution SAR images based on model fusion and half-precision parallel inference. J-STARS 15, 4861\u20134872 (2022)","journal-title":"J-STARS"},{"key":"37_CR25","first-page":"5564","volume":"32","author":"C Yu","year":"2023","unstructured":"Yu, C., Liu, Y., Zhao, J., Wu, S., Hu, Z.: Feature interaction learning network for cross-spectral image patch matching. TIP 32, 5564\u20135579 (2023)","journal-title":"TIP"},{"key":"37_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, J., Shi, Z., Yu, C., Liu, Y.: Multi-scale direction-aware network for infrared small target detection. arXiv preprint arXiv:2406.02037 (2024)","DOI":"10.1109\/TGRS.2025.3612039"},{"issue":"6","key":"37_CR28","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2019)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5628-1_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:30:21Z","timestamp":1768944621000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5628-1_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556274","9789819556281"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5628-1_37","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":"21 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}