Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering
Abstract Hyperspectral images have a special attribute with both spectral and spatial information, which is of great significance for the evaluation of the stealth performance of camouflaged targets. Aiming at the problems of a single evaluation index and the low credibility of traditional optical c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-023-00971-x |
| _version_ | 1797945688985698304 |
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| author | Zhenxin He Yuanying Gan Shixin Ma Chuntong Liu Zhongye Liu |
| author_facet | Zhenxin He Yuanying Gan Shixin Ma Chuntong Liu Zhongye Liu |
| author_sort | Zhenxin He |
| collection | DOAJ |
| description | Abstract Hyperspectral images have a special attribute with both spectral and spatial information, which is of great significance for the evaluation of the stealth performance of camouflaged targets. Aiming at the problems of a single evaluation index and the low credibility of traditional optical camouflage evaluation methods, this paper proposes a grayscale clustering camouflage effect evaluation method based on multifeature descriptions of hyperspectral images using similarity indicators that reflect different spectral characteristics of the target and background. From the perspective of spectrum and human visual contrast, a comprehensive evaluation index system including spectral distance feature, spectral derivative feature, curve shape feature and spatial texture feature is constructed by combining spatial–spectral multi-feature constraints. At the same time, an improved Delphi method is proposed to simulate the expert decision-making process, and better evaluation weights are obtained by comparison and screening. The comprehensive evaluation of camouflage effect based on whitening function gray clustering is realized. The proposed method can not only give the “excellent” and “bad” of camouflage effect qualitatively, but also calculate the comprehensive score of camouflage effect by model. |
| first_indexed | 2024-04-10T20:59:04Z |
| format | Article |
| id | doaj.art-a25100c2573c4b758c5336888ae7eef1 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-10T20:59:04Z |
| publishDate | 2023-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-a25100c2573c4b758c5336888ae7eef12023-01-22T12:28:28ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802023-01-012023111610.1186/s13634-023-00971-xEvaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clusteringZhenxin He0Yuanying Gan1Shixin Ma2Chuntong Liu3Zhongye Liu4School of Missile Engineering, PLA Rocket Force University of EngineeringSchool of Missile Engineering, PLA Rocket Force University of EngineeringSchool of Missile Engineering, PLA Rocket Force University of EngineeringSchool of Missile Engineering, PLA Rocket Force University of EngineeringSchool of Missile Engineering, PLA Rocket Force University of EngineeringAbstract Hyperspectral images have a special attribute with both spectral and spatial information, which is of great significance for the evaluation of the stealth performance of camouflaged targets. Aiming at the problems of a single evaluation index and the low credibility of traditional optical camouflage evaluation methods, this paper proposes a grayscale clustering camouflage effect evaluation method based on multifeature descriptions of hyperspectral images using similarity indicators that reflect different spectral characteristics of the target and background. From the perspective of spectrum and human visual contrast, a comprehensive evaluation index system including spectral distance feature, spectral derivative feature, curve shape feature and spatial texture feature is constructed by combining spatial–spectral multi-feature constraints. At the same time, an improved Delphi method is proposed to simulate the expert decision-making process, and better evaluation weights are obtained by comparison and screening. The comprehensive evaluation of camouflage effect based on whitening function gray clustering is realized. The proposed method can not only give the “excellent” and “bad” of camouflage effect qualitatively, but also calculate the comprehensive score of camouflage effect by model.https://doi.org/10.1186/s13634-023-00971-xHyperspectral imageCamouflage evaluationWhitening weightGrayscale clustering |
| spellingShingle | Zhenxin He Yuanying Gan Shixin Ma Chuntong Liu Zhongye Liu Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering EURASIP Journal on Advances in Signal Processing Hyperspectral image Camouflage evaluation Whitening weight Grayscale clustering |
| title | Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering |
| title_full | Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering |
| title_fullStr | Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering |
| title_full_unstemmed | Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering |
| title_short | Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering |
| title_sort | evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering |
| topic | Hyperspectral image Camouflage evaluation Whitening weight Grayscale clustering |
| url | https://doi.org/10.1186/s13634-023-00971-x |
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