Yüksek Boyutlu Model Gösterilimi ile Hiperspektral Görüntülerde Anomali Tespiti

Translated title of the contribution: Anomaly Detection in Hyperspectral Images with High Dimensional Model Representation

Derya Bodur, Evrim Korkmaz Özay, Burcu Tunga

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Hyperspectral imaging (HSI) is an important imaging technology that enables the high sensitivity and accuracy analysis of wide areas through its multispectral band structure. Anomaly detection in hyperspectral images can be defined as the detection of pixels that do not belong to the background and whose spectral properties are unknown. In this study, a method based on High Dimensional Model Representation (HDMR) is proposed for detecting anomalies in hyperspectral images. The effectiveness of the proposed method has been tested on a sample image and compared with commonly used anomaly detection methods. The HDMR-based anomaly detection algorithm has shown to be more effective in suppressing the background compared to other methods by making anomaly pixels more visible.

Translated title of the contributionAnomaly Detection in Hyperspectral Images with High Dimensional Model Representation
Original languageTurkish
Title of host publication32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350388961
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Mersin, Turkey
Duration: 15 May 202418 May 2024

Publication series

Name32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings

Conference

Conference32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024
Country/TerritoryTurkey
CityMersin
Period15/05/2418/05/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

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