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Robust Hyperspectral Anomaly Detection via Unsupervised Multiscale Feature Fusion.

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Title: Robust Hyperspectral Anomaly Detection via Unsupervised Multiscale Feature Fusion.
Authors: Wang, Yihan1,2 (AUTHOR), Mu, Zhenhua2,3,4 (AUTHOR), Zhang, Hanyu2,3 (AUTHOR), Song, Chuanming3,4 (AUTHOR), Wang, Xianghai1,4 (AUTHOR) xhwang@lnnu.edu.cn
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1554. 18p.
Subjects: Feature extraction, Signal convolution, Image analysis, Feature selection, Remote sensing, Outlier detection
Abstract: Highlights: What are the main findings? A robust unsupervised multi-scale feature fusion network (UMF2Net) is developed for HSI-AD. Central difference convolution and 3D convolution feature weighting are integrated to capture multi-scale spatial-spectral signatures. What are the implications of the main findings? By effectively fusing hierarchical features, UMF2Net achieved competitive results in four different remote sensing scenarios. The framework provides a robust solution for detecting anomalies of varying sizes without requiring prior target information. In hyperspectral image (HSI) anomaly detection (AD) methods, detecting small targets or anomalies remains challenging. This difficulty arises because targets or anomalies may vary significantly in size, shape, and texture, causing them to be obscured by larger-scale background features. To address the above issue, this paper proposes an unsupervised multi-scale feature fusion network (UMF2Net) for HSI-AD. Firstly, central difference convolution analyzes the image at multiple scales to capture fine-to-coarse details and structural information. Additionally, three-dimensional (3D) convolution is employed to generate feature weights for the multi-scale features, assigning different weights to features with different contributions so that the model dynamically emphasizes features that have a greater impact on the AD results. Finally, by using the two proposed multi-scale feature fusion modules, the model effectively integrates features at different scales, thereby enhancing its ability to detect anomalies of varying sizes. Compared with several classical HSI-AD algorithms on real hyperspectral datasets from four scenarios, UMF2Net achieved competitive detection results, verifying the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Robust Hyperspectral Anomaly Detection via Unsupervised Multiscale Feature Fusion.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yihan%22">Wang, Yihan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mu%2C+Zhenhua%22">Mu, Zhenhua</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hanyu%22">Zhang, Hanyu</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Chuanming%22">Song, Chuanming</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xianghai%22">Wang, Xianghai</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> xhwang@lnnu.edu.cn</i>
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  Data: Highlights: What are the main findings? A robust unsupervised multi-scale feature fusion network (UMF2Net) is developed for HSI-AD. Central difference convolution and 3D convolution feature weighting are integrated to capture multi-scale spatial-spectral signatures. What are the implications of the main findings? By effectively fusing hierarchical features, UMF2Net achieved competitive results in four different remote sensing scenarios. The framework provides a robust solution for detecting anomalies of varying sizes without requiring prior target information. In hyperspectral image (HSI) anomaly detection (AD) methods, detecting small targets or anomalies remains challenging. This difficulty arises because targets or anomalies may vary significantly in size, shape, and texture, causing them to be obscured by larger-scale background features. To address the above issue, this paper proposes an unsupervised multi-scale feature fusion network (UMF2Net) for HSI-AD. Firstly, central difference convolution analyzes the image at multiple scales to capture fine-to-coarse details and structural information. Additionally, three-dimensional (3D) convolution is employed to generate feature weights for the multi-scale features, assigning different weights to features with different contributions so that the model dynamically emphasizes features that have a greater impact on the AD results. Finally, by using the two proposed multi-scale feature fusion modules, the model effectively integrates features at different scales, thereby enhancing its ability to detect anomalies of varying sizes. Compared with several classical HSI-AD algorithms on real hyperspectral datasets from four scenarios, UMF2Net achieved competitive detection results, verifying the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Outlier detection
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              M: 05
              Text: May2026
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              Y: 2026
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