{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:27:58Z","timestamp":1760149678932,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T00:00:00Z","timestamp":1693267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62071233","61971223","61976117","BK20211570","BK20180018","BK20191409","30917015104","30919011103","30919011402","30921011209","JSGP202204","19KJA360001"],"award-info":[{"award-number":["62071233","61971223","61976117","BK20211570","BK20180018","BK20191409","30917015104","30919011103","30919011402","30921011209","JSGP202204","19KJA360001"]}]},{"name":"Jiangsu Provincial Natural Science Foundation of China","award":["62071233","61971223","61976117","BK20211570","BK20180018","BK20191409","30917015104","30919011103","30919011402","30921011209","JSGP202204","19KJA360001"],"award-info":[{"award-number":["62071233","61971223","61976117","BK20211570","BK20180018","BK20191409","30917015104","30919011103","30919011402","30921011209","JSGP202204","19KJA360001"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62071233","61971223","61976117","BK20211570","BK20180018","BK20191409","30917015104","30919011103","30919011402","30921011209","JSGP202204","19KJA360001"],"award-info":[{"award-number":["62071233","61971223","61976117","BK20211570","BK20180018","BK20191409","30917015104","30919011103","30919011402","30921011209","JSGP202204","19KJA360001"]}]},{"name":"Key Projects of University Natural Science Fund of Jiangsu Province","award":["62071233","61971223","61976117","BK20211570","BK20180018","BK20191409","30917015104","30919011103","30919011402","30921011209","JSGP202204","19KJA360001"],"award-info":[{"award-number":["62071233","61971223","61976117","BK20211570","BK20180018","BK20191409","30917015104","30919011103","30919011402","30921011209","JSGP202204","19KJA360001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the constrained processing capabilities of real-time detection techniques in remote sensing applications, it is often difficult to obtain detection results with high accuracy in practice. To address this problem, we introduce a new real-time anomaly detection algorithm for hyperspectral images called cloud\u2013edge RX (CE-RX). The algorithm combines the advantages of cloud and edge computing. During the data acquisition process, the edge performs real-time detection on the data just captured to obtain a coarse result and find the suspicious anomalies. At regular intervals, the suspicious anomalies are sent to the cloud for further detection with a highly accurate algorithm, then the cloud sends back the (high-accuracy) results to the edge for information updating. After receiving the results from the cloud, the edge updates the information of the detector in the real-time algorithm to improve the detection accuracy of the next acquired piece of data. Our experimental results demonstrate that the proposed cloud\u2013edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms.<\/jats:p>","DOI":"10.3390\/rs15174242","type":"journal-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T08:51:14Z","timestamp":1693299074000},"page":"4242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7681-7773","authenticated-orcid":false,"given":"Yunchang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Jiang","family":"Cai","sequence":"additional","affiliation":[{"name":"Nanjing Research Institute of Electronics Engineering (NRIEE), Nanjing 210007, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7734-4077","authenticated-orcid":false,"given":"Junlong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Jin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Zhihui","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2384-9141","authenticated-orcid":false,"given":"Javier","family":"Plaza","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, 10071 C\u00e1ceres, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-1659","authenticated-orcid":false,"given":"Antonio","family":"Plaza","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, 10071 C\u00e1ceres, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7162-0202","authenticated-orcid":false,"given":"Zebin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.1109\/TGRS.2011.2108305","article-title":"Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Ant Colony Optimization (ACO) Algorithm","volume":"49","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1109\/JSTARS.2013.2272633","article-title":"Analysis of the proportion of surface reflected radiance in mid-infrared absorption bands","volume":"7","author":"Zhang","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1109\/JSTARS.2012.2227245","article-title":"A Comparative Study on Linear Regression-Based Noise Estimation for Hyperspectral Imagery","volume":"6","author":"Gao","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep learning for hyperspectral image classification: An overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"5527913","article-title":"Meta-learning based hyperspectral target detection using Siamese network","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5529022","DOI":"10.1109\/TGRS.2022.3176856","article-title":"Multiobjective optimization-based hyperspectral band selection for target detection","volume":"60","author":"Song","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5540224","DOI":"10.1109\/TGRS.2022.3208519","article-title":"Background-Annihilated Target-Constrained Interference-Minimized Filter (TCIMF) for Hyperspectral Target Detection","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5544915","DOI":"10.1109\/TGRS.2022.3225902","article-title":"Global to local: A hierarchical detection algorithm for hyperspectral image target detection","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"5506818","article-title":"A Multidepth and Multibranch Network for Hyperspectral Target Detection Based on Band Selection","volume":"61","author":"Gao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5515516","DOI":"10.1109\/TGRS.2023.3288739","article-title":"Hyperspectral Target Detection via Spectral Aggregation and Separation Network with Target Band Random Mask","volume":"61","author":"Gao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2021.03.035","article-title":"A survey: Deep learning for hyperspectral image classification with few labeled samples","volume":"448","author":"Jia","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xie, H., Chen, Y., and Ghamisi, P. (2021). Remote sensing image scene classification via label augmentation and intra-class constraint. Remote Sens., 13.","DOI":"10.3390\/rs13132566"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3669","DOI":"10.1109\/JSTARS.2021.3063679","article-title":"DFL-LC: Deep feature learning with label consistencies for hyperspectral image classification","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TGRS.2014.2343955","article-title":"Collaborative representation for hyperspectral anomaly detection","volume":"53","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3889","DOI":"10.1109\/TCYB.2021.3065070","article-title":"Weakly supervised low-rank representation for hyperspectral anomaly detection","volume":"51","author":"Xie","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6504","DOI":"10.1109\/TNNLS.2021.3082158","article-title":"Weakly supervised discriminative learning with spectral constrained generative adversarial network for hyperspectral anomaly detection","volume":"33","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/29.60107","article-title":"Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution","volume":"38","author":"Reed","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1109\/78.229895","article-title":"Comparative performance analysis of adaptive multispectral detectors","volume":"41","author":"Yu","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_19","first-page":"5513412","article-title":"LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"5505016","article-title":"Hyperspectral anomaly detection based on chessboard topology","volume":"61","author":"Gao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"5507517","article-title":"Learning double subspace representation for joint hyperspectral anomaly detection and noise removal","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1109\/JSTARS.2019.2919911","article-title":"A novel hyperspectral anomaly detection algorithm for real-time applications with push-broom sensors","volume":"12","author":"Horstrand","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"B\u00e1scones, D., Gonz\u00e1lez, C., and Mozos, D. (2020). An FPGA accelerator for real-time lossy compression of hyperspectral images. Remote Sens., 12.","DOI":"10.3390\/rs12162563"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.1007\/s11554-017-0679-2","article-title":"FPGA implementation of a maximum simplex volume algorithm for endmember extraction from remotely sensed hyperspectral images","volume":"16","author":"Li","year":"2019","journal-title":"J. Real-Time Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0031-3203(02)00065-1","article-title":"Real-time constrained linear discriminant analysis to target detection and classification in hyperspectral imagery","volume":"36","author":"Du","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/36.917889","article-title":"Real-time processing algorithms for target detection and classification in hyperspectral imagery","volume":"39","author":"Chang","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.3390\/rs70403966","article-title":"Global and local real-time anomaly detectors for hyperspectral remote sensing imagery","volume":"7","author":"Zhao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/TAES.2014.130065","article-title":"Real-time causal processing of anomaly detection for hyperspectral imagery","volume":"50","author":"Chen","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, S.Y., Wu, C.C., Liu, C., and Chang, C.I. (2012, January 26\u201327). Real-time causal processing of anomaly detection. Proceedings of the High-Performance Computing in Remote Sensing II. SPIE, Edinburgh, UK.","DOI":"10.1117\/12.979179"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103325","DOI":"10.1016\/j.infrared.2020.103325","article-title":"Real-time kernel collaborative representation-based anomaly detection for hyperspectral imagery","volume":"107","author":"Zhao","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1109\/TMC.2019.2908403","article-title":"Joint communication, computation, caching, and control in big data multi-access edge computing","volume":"19","author":"Ndikumana","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/JIOT.2017.2767608","article-title":"Future edge cloud and edge computing for internet of things applications","volume":"5","author":"Pan","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1109\/JIOT.2018.2805263","article-title":"Edge computing for the Internet of Things: A case study","volume":"5","author":"Premsankar","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/TNET.2020.2979807","article-title":"Efficient computing resource sharing for mobile edge-cloud computing networks","volume":"28","author":"Zhang","year":"2020","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.future.2018.12.055","article-title":"A computation offloading method over big data for IoT-enabled cloud-edge computing","volume":"95","author":"Xu","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1007\/s11276-019-02127-y","article-title":"Multi-objective computation offloading for internet of vehicles in cloud-edge computing","volume":"26","author":"Xu","year":"2020","journal-title":"Wirel. Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1109\/TVT.2019.2904244","article-title":"Collaborative cloud and edge computing for latency minimization","volume":"68","author":"Ren","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jia, M., Cao, J., and Yang, L. (May, January 27). Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing. Proceedings of the 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Toronto, ON, Canada.","DOI":"10.1109\/INFCOMW.2014.6849257"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/TCAD.2020.3046665","article-title":"Efficient federated learning for cloud-based AIoT applications","volume":"40","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TSUSC.2019.2955317","article-title":"A collaborative and sustainable edge-cloud architecture for object tracking with convolutional siamese networks","volume":"6","author":"Gu","year":"2019","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1039\/C1JA10241A","article-title":"Push-broom hyperspectral imaging for elemental mapping with glow discharge optical emission spectrometry","volume":"27","author":"Gamez","year":"2012","journal-title":"J. Anal. At. Spectrom."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4614","DOI":"10.1109\/JSTARS.2017.2725382","article-title":"Fast real-time causal linewise progressive hyperspectral anomaly detection via cholesky decomposition","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/JSTARS.2013.2238609","article-title":"Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data","volume":"6","author":"Molero","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/TGRS.2002.800280","article-title":"Anomaly detection and classification for hyperspectral imagery","volume":"40","author":"Chang","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5425","DOI":"10.1109\/TAP.2018.2854162","article-title":"Analysis of partial geometry modification problems using the partitioned-inverse formula and Sherman\u2013Morrison\u2013Woodbury formula-based method","volume":"66","author":"Chen","year":"2018","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_46","first-page":"183","article-title":"Generalization of the Sherman\u2013Morrison\u2013Woodbury formula involving the Schur complement","volume":"309","author":"Xu","year":"2017","journal-title":"Appl. Math. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5131","DOI":"10.1109\/TGRS.2020.3021671","article-title":"An effective evaluation tool for hyperspectral target detection: 3D receiver operating characteristic curve analysis","volume":"59","author":"Chang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1109\/TGRS.2015.2493201","article-title":"Anomaly detection in hyperspectral images based on low-rank and sparse representation","volume":"54","author":"Xu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fan, G., Ma, Y., Huang, J., Mei, X., and Ma, J. (2021, January 6\u201311). Robust graph autoencoder for hyperspectral anomaly detection. Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414767"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tan, K., Hou, Z., Wu, F., Du, Q., and Chen, Y. (2019). Anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation. Remote Sens., 11.","DOI":"10.3390\/rs11111318"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/LGRS.2020.2998809","article-title":"Hyperspectral anomaly detection via integration of feature extraction and background purification","volume":"18","author":"Ma","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4242\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:41:50Z","timestamp":1760128910000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,29]]},"references-count":51,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174242"],"URL":"https:\/\/doi.org\/10.3390\/rs15174242","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,8,29]]}}}