CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images
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 image...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/17/4242 |
| _version_ | 1797581921705787392 |
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| author | Yunchang Wang Jiang Cai Junlong Zhou Jin Sun Yang Xu Yi Zhang Zhihui Wei Javier Plaza Antonio Plaza Zebin Wu |
| author_facet | Yunchang Wang Jiang Cai Junlong Zhou Jin Sun Yang Xu Yi Zhang Zhihui Wei Javier Plaza Antonio Plaza Zebin Wu |
| author_sort | Yunchang Wang |
| collection | DOAJ |
| description | 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–edge 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–edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms. |
| first_indexed | 2024-03-10T23:14:32Z |
| format | Article |
| id | doaj.art-8eb797c2f1d0424a8b2c3d915bf1bf4e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:14:32Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8eb797c2f1d0424a8b2c3d915bf1bf4e2023-11-19T08:46:27ZengMDPI AGRemote Sensing2072-42922023-08-011517424210.3390/rs15174242CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral ImagesYunchang Wang0Jiang Cai1Junlong Zhou2Jin Sun3Yang Xu4Yi Zhang5Zhihui Wei6Javier Plaza7Antonio Plaza8Zebin Wu9School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaNanjing Research Institute of Electronics Engineering (NRIEE), Nanjing 210007, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaHyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, 10071 Cáceres, SpainHyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, 10071 Cáceres, SpainSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaDue 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–edge 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–edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms.https://www.mdpi.com/2072-4292/15/17/4242hyperspectralanomaly detectioncloud–edge collaborationreal-time detection |
| spellingShingle | Yunchang Wang Jiang Cai Junlong Zhou Jin Sun Yang Xu Yi Zhang Zhihui Wei Javier Plaza Antonio Plaza Zebin Wu CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images Remote Sensing hyperspectral anomaly detection cloud–edge collaboration real-time detection |
| title | CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images |
| title_full | CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images |
| title_fullStr | CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images |
| title_full_unstemmed | CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images |
| title_short | CE-RX: A Collaborative Cloud-Edge Anomaly Detection Approach for Hyperspectral Images |
| title_sort | ce rx a collaborative cloud edge anomaly detection approach for hyperspectral images |
| topic | hyperspectral anomaly detection cloud–edge collaboration real-time detection |
| url | https://www.mdpi.com/2072-4292/15/17/4242 |
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