{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:08:04Z","timestamp":1771063684418,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Science and Technology","award":["AA19254016"],"award-info":[{"award-number":["AA19254016"]}]},{"name":"Guangxi Science and Technology","award":["2023158004"],"award-info":[{"award-number":["2023158004"]}]},{"name":"Beihai Science and Technology Bureau Project","award":["AA19254016"],"award-info":[{"award-number":["AA19254016"]}]},{"name":"Beihai Science and Technology Bureau Project","award":["2023158004"],"award-info":[{"award-number":["2023158004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Remote sensing image change detection is a core task of remote sensing image analysis; its purpose is to identify and quantify land cover changes in different periods. However, when the existing methods deal with complex features and subtle changes in buildings, vegetation, water bodies, roads, and other ground objects, there are often problems of false detection and missing detection, which affect the detection accuracy. To improve the accuracy of change detection, a multi-scale feature fusion network based on difference enhancement (FEDNet) is proposed. The FEDNet consists of a difference enhancement module (DEM) and a multi-scale feature fusion module (MFM). By summing the variation features of two-phase remote sensing images, the DEM enhances pixel-level differences, captures subtle changes, and aggregates features. The MFM fully integrates the multi-stage deep semantic information, which enables better extraction of changing features in complex scenes. Experiments on the LEVIR-CD, CLCD, WHU, NJDS, and GBCNR datasets show that the FEDNet significantly improves the detection efficiency of changes in buildings, cities, and vegetation. In terms of F1 value, IoU (Intersection over Union), precision, and recall rate, the FEDNet is superior to existing methods, which verifies its excellent performance.<\/jats:p>","DOI":"10.3390\/sym17040590","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T09:06:51Z","timestamp":1744621611000},"page":"590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-Scale Feature Fusion Based on Difference Enhancement for Remote Sensing Image Change Detection"],"prefix":"10.3390","volume":"17","author":[{"given":"Haoyuan","family":"Hou","sequence":"first","affiliation":[{"name":"School of Electronic Information, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Architecture and Civil Engineering City Unive, Guilin University of Electronic Technologyrsity of Hong Kong, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1743-3139","authenticated-orcid":false,"given":"Qin","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Guilin University of Electronic Technology, Beihai 536000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1845-5623","authenticated-orcid":false,"given":"Tonglai","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A general end-to-end 2-D CNN framework for hyperspectral image change detection","volume":"57","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Le Saux, B., Boulch, A., and Gousseau, Y. (2018, January 22\u201327). Urban change detection for multispectral earth observation using convolutional neural networks. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518015"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","article-title":"Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model","volume":"18","author":"Liu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","first-page":"8007805","article-title":"SNUNet-CD: A densely connected Siamese network for change detection of VHR images","volume":"19","author":"Fang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, J., Luo, C., Chen, X., Wei, S., and Luo, Y. (2021). Remote sensing change detection based on multidirectional adaptive feature fusion and perceptual similarity. Remote Sens., 13.","DOI":"10.3390\/rs13153053"},{"key":"ref_8","first-page":"5607514","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bandara WG, C., and Patel, V.M. (2022, January 17\u201322). A transformer-based siamese network for change detection. Proceedings of the IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysiam.","DOI":"10.1109\/IGARSS46834.2022.9883686"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.1109\/JSTARS.2022.3177235","article-title":"A CNN-transformer network with multiscale context aggregation for fine-grained cropland change detection","volume":"15","author":"Liu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.isprsjprs.2023.07.001","article-title":"An attention-based multiscale transformer network for remote sensing image change detection","volume":"202","author":"Liu","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","first-page":"230","article-title":"Remote sensing image change detection based on difference enhancement and dual-attention Transformer","volume":"54","author":"Zhang","year":"2019","journal-title":"Radio Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 8\u201316). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Amsterdam, The Netherlands.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112134","DOI":"10.1016\/j.ymssp.2024.112134","article-title":"Surrogate modeling of pantograph-catenary system interactions","volume":"224","author":"Cheng","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2020.3016905","article-title":"Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing","volume":"38","author":"Monga","year":"2021","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110676","DOI":"10.1016\/j.patcog.2024.110676","article-title":"DIVA: Deep unfolded network from quantum interactive patches for image restoration","volume":"155","author":"Dutta","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5610111","DOI":"10.1109\/TGRS.2023.3277496","article-title":"Changer: Feature interaction is what you need for change detection","volume":"61","author":"Fang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_20","first-page":"5622519","article-title":"TransUNetCD: A hybrid transformer network for change detection in optical remote-sensing images","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5224713","DOI":"10.1109\/TGRS.2022.3221492","article-title":"SwinSUNet: Pure transformer network for remote sensing image change detection","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.5194\/essd-13-3907-2021","article-title":"30 m annual land cover and its dynamics in china from 1990 to 2019","volume":"13","author":"Yang","year":"2021","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery dataset","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote. Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2022.05.001","article-title":"Semantic feature-constrained multitask siamese network for building change detection in high-spatial resolution remote sensing imagery","volume":"189","author":"Shen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1147\/rd.24.0314","article-title":"A business intelligence system","volume":"2","author":"Luhn","year":"1958","journal-title":"IBM J. Res. Dev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1108\/eb050097","article-title":"The Cranfield tests on index language devices","volume":"19","author":"Cleverdon","year":"1967","journal-title":"Aslib Proc."},{"key":"ref_28","first-page":"1","article-title":"Information retrieval: Theory and practice","volume":"79","year":"1979","journal-title":"Proc. Jt. IBM\/Univ. Newctle. Upon Tyne Semin. Data Base Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_31","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of theMedical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/590\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:13:20Z","timestamp":1760030000000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/590"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,12]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040590"],"URL":"https:\/\/doi.org\/10.3390\/sym17040590","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,12]]}}}