{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T15:26:14Z","timestamp":1769354774272,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T00:00:00Z","timestamp":1666915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long Short-Term Memory (BiLSTM) method to reduce the overfitting problem and increase the performance of classification in change detection applications. Additionally, data usage and accuracy in remote sensing activities, particularly CD, can be significantly improved by a large number of training models based on BiLSTM. Normalization techniques are applied to input images in order to enhance the quality and reduce the difference in pixel value. The AlexNet and VGG16 models were used to extract useful features from the normalized images. The extracted features were then applied to the FWA-BiLSTM model, to give more weight to the unique features and increase the efficiency of classification. The attention layer selects the unique features that help to distinguish the changes in the remote sensing images. From the experimental results, it was clearly shown that the proposed FWA-BiLSTM model achieved better performance in terms of precision (93.43%), recall (93.16%), and overall accuracy (99.26%), when compared with the existing Difference-enhancement Dense-attention Convolutional Neural Network (DDCNN) model.<\/jats:p>","DOI":"10.3390\/rs14215402","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T09:01:42Z","timestamp":1667120502000},"page":"5402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Feature Weighted Attention\u2014Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Raj Kumar","family":"Patra","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad 501401, India"}]},{"given":"Sujata N.","family":"Patil","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, KLE Dr. M S Sheshgiri College of Engineering and Technology, Karnataka 590008, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8920-6969","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Falkowski-Gilski","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11\/12, 80-233 Gdansk, Poland"}]},{"given":"Zbigniew","family":"\u0141ubniewski","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11\/12, 80-233 Gdansk, Poland"}]},{"given":"Rachana","family":"Poongodan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, New Horizon College of Engineering, Bangalore 560103, India"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","first-page":"102597","article-title":"SUACDNet: Attentional change detection network based on Siamese U-shaped structure","volume":"105","author":"Song","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","first-page":"4403718","article-title":"Super-resolution-based change detection network with stacked attention module for images with different resolutions","volume":"60","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","unstructured":"Zhang, H., Lin, M., Yang, G., and Zhang, L. (2021). ESCNET: An end-to-end superpixel-enhanced change detection network for very-high-resolution remote sensing images. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2021.05.001","article-title":"High-resolution triplet network with dynamic multiscale feature for change detection on satellite images","volume":"177","author":"Hou","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.neucom.2021.06.059","article-title":"Fully convolutional Siamese networks based change detection for optical aerial images with focal contrastive loss","volume":"457","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.isprsjprs.2021.05.002","article-title":"Object-level change detection with a dual correlation attention-guided detector","volume":"177","author":"Zhang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5401410","DOI":"10.1109\/TGRS.2021.3069889","article-title":"Hybrid-scale self-similarity exploitation for remote sensing image super-resolution","volume":"60","author":"Lei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fu, L., Li, Y., and Zhang, Y. (2021). HDFNET: Hierarchical dynamic fusion network for change detection in optical aerial images. Remote Sens., 13.","DOI":"10.3390\/rs13081440"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Srinivas, M., Roy, D., and Mohan, C.K. (2016, January 20\u201325). Discriminative feature extraction from X-ray images using deep convolutional neural networks. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7471809"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ijjina, E.P., and Mohan, C.K. (2014, January 3\u20136). Human action recognition based on recognition of linear patterns in action bank features using convolutional neural networks. Proceedings of the 2014 13th International Conference on Machine Learning and Applications, Detroit, MI, USA.","DOI":"10.1109\/ICMLA.2014.33"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Saini, R., Jha, N.K., Das, B., Mittal, S., and Mohan, C.K. (2020, January 1\u20135). ULSAM: Ultra-lightweight subspace attention module for compact convolutional neural networks. Proceedings of the 2020 IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093341"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11760-020-01740-1","article-title":"Residual spatiotemporal autoencoder for unsupervised video anomaly detection","volume":"15","author":"Deepak","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1109\/TMM.2018.2887021","article-title":"Unsupervised universal attribute modeling for action recognition","volume":"21","author":"Roy","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5575","DOI":"10.1109\/TIP.2018.2856373","article-title":"Spontaneous expression recognition using universal attribute model","volume":"27","author":"Perveen","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Roy, D., Ishizaka, T., Mohan, C.K., and Fukuda, A. (2019, January 27\u201330). Vehicle trajectory prediction at intersections using interaction based generative adversarial networks. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8916927"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.patrec.2018.03.004","article-title":"Snatch theft detection in unconstrained surveillance videos using action attribute modelling","volume":"108","author":"Roy","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1007\/s42979-021-00591-y","article-title":"Offline signature recognition using image processing techniques and back propagation neuron network system","volume":"2","author":"Kiran","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2021.07.007","article-title":"A deep translation (GAN) based change detection network for optical and SAR remote sensing images","volume":"179","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","first-page":"102348","article-title":"ADS-Net: An attention-based deeply supervised network for remote sensing image change detection","volume":"101","author":"Wang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"5604816","DOI":"10.1109\/TGRS.2022.3158741","article-title":"A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection","volume":"60","author":"Shi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2021.03.005","article-title":"CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery","volume":"175","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"DASNet: Dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images","volume":"14","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, X., Li, K., Zhang, J., Gong, J., and Zhang, M. (2020). PGA-SiamNet: Pyramid feature-based attention-guided Siamese network for remote sensing orthoimagery building change detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_28","first-page":"8017505","article-title":"A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7296","DOI":"10.1109\/TGRS.2020.3033009","article-title":"Optical remote sensing image change detection based on attention mechanism and image difference","volume":"59","author":"Peng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Song, K., Cui, F., and Jiang, J. (2021). An Efficient Lightweight Neural Network for Remote Sensing Image Change Detection. Remote Sens., 13.","DOI":"10.3390\/rs13245152"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105878","DOI":"10.1016\/j.cmpb.2020.105878","article-title":"Medical image segmentation and reconstruction of prostate tumor based on 3D AlexNet","volume":"200","author":"Chen","year":"2021","journal-title":"Comput. Meth. Prog. Biomed."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10799","DOI":"10.1007\/s00521-020-05082-4","article-title":"Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm","volume":"33","author":"Lu","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, H.C., Widodo, A.M., Wisnujati, A., Rahaman, M., Lin, J.C.W., Chen, L., and Weng, C.E. (2022). AlexNet convolutional neural network for disease detection and classification of tomato leaf. Electronics, 11.","DOI":"10.3390\/electronics11060951"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, P., Zhao, J., and Zhang, J. (2021). Identification of intrinsically disordered protein regions based on deep neural network-VGG16. Algorithms, 14.","DOI":"10.3390\/a14040107"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Montaha, S., Azam, S., Rafid, A.K.M.R.H., Ghosh, P., Hasan, M.Z., Jonkman, M., and De Boer, F. (2021). BreastNet18: A high accuracy fine-tuned VGG16 model evaluated using ablation study for diagnosing breast cancer from enhanced mammography images. Biology, 10.","DOI":"10.3390\/biology10121347"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101310","DOI":"10.1016\/j.ecoinf.2021.101310","article-title":"Analysis on change detection techniques for remote sensing applications: A review","volume":"63","author":"Afaq","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s12145-021-00723-1","article-title":"An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network","volume":"15","author":"Singla","year":"2022","journal-title":"Earth Sci. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4741","DOI":"10.1007\/s00521-020-05532-z","article-title":"A CNN-BiLSTM-AM method for stock price prediction","volume":"33","author":"Lu","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"565","DOI":"10.5194\/isprs-archives-XLII-2-565-2018","article-title":"Change detection in remote sensing images using conditional adversarial networks","volume":"42","author":"Lebedev","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, L., Chen, Y., Song, S., Li, F., and Huang, G. (2021). Deep Siamese networks-based change detection with remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13173394"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:04:43Z","timestamp":1760144683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,28]]},"references-count":41,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215402"],"URL":"https:\/\/doi.org\/10.3390\/rs14215402","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,28]]}}}