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Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery
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| Zeitschriftentitel: | Remote Sensing |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | Remote Sensing, 12, 2020, 1, S. 142 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
|
| Schlagwörter: |
| author_facet |
Lyu, Yuxiao Peng, Lingbing Pu, Tian Yang, Chunping Wang, Jun Peng, Zhenming Lyu, Yuxiao Peng, Lingbing Pu, Tian Yang, Chunping Wang, Jun Peng, Zhenming |
|---|---|
| author |
Lyu, Yuxiao Peng, Lingbing Pu, Tian Yang, Chunping Wang, Jun Peng, Zhenming |
| spellingShingle |
Lyu, Yuxiao Peng, Lingbing Pu, Tian Yang, Chunping Wang, Jun Peng, Zhenming Remote Sensing Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery General Earth and Planetary Sciences |
| author_sort |
lyu, yuxiao |
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Lyu, Yuxiao Peng, Lingbing Pu, Tian Yang, Chunping Wang, Jun Peng, Zhenming 2072-4292 MDPI AG General Earth and Planetary Sciences http://dx.doi.org/10.3390/rs12010142 <jats:p>In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.</jats:p> Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery Remote Sensing |
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| title |
Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_unstemmed |
Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_full |
Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_fullStr |
Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_full_unstemmed |
Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_short |
Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_sort |
cirrus detection based on rpca and fractal dictionary learning in infrared imagery |
| topic |
General Earth and Planetary Sciences |
| url |
http://dx.doi.org/10.3390/rs12010142 |
| publishDate |
2020 |
| physical |
142 |
| description |
<jats:p>In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.</jats:p> |
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| author | Lyu, Yuxiao, Peng, Lingbing, Pu, Tian, Yang, Chunping, Wang, Jun, Peng, Zhenming |
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| description | <jats:p>In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.</jats:p> |
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| spelling | Lyu, Yuxiao Peng, Lingbing Pu, Tian Yang, Chunping Wang, Jun Peng, Zhenming 2072-4292 MDPI AG General Earth and Planetary Sciences http://dx.doi.org/10.3390/rs12010142 <jats:p>In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.</jats:p> Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery Remote Sensing |
| spellingShingle | Lyu, Yuxiao, Peng, Lingbing, Pu, Tian, Yang, Chunping, Wang, Jun, Peng, Zhenming, Remote Sensing, Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery, General Earth and Planetary Sciences |
| title | Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_full | Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_fullStr | Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_full_unstemmed | Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_short | Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| title_sort | cirrus detection based on rpca and fractal dictionary learning in infrared imagery |
| title_unstemmed | Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery |
| topic | General Earth and Planetary Sciences |
| url | http://dx.doi.org/10.3390/rs12010142 |