Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect
With the successful launch of China’s GF series satellites, it is more important to study the image data quality, the adaptability of processing method and information extraction method. The panchromatic and multi-spectral data which is based on the GF-2 images data of Chinese sub-meter high-resolut...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad Nacional de Colombia
2019-04-01
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| Series: | Earth Sciences Research Journal |
| Subjects: | |
| Online Access: | https://revistas.unal.edu.co/index.php/esrj/article/view/80281 |
| _version_ | 1818977739024105472 |
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| author | Jintong Ren Wunian Yang Xin Yang Xiaoyu Deng He Zhao Fang Wang Lei Wang |
| author_facet | Jintong Ren Wunian Yang Xin Yang Xiaoyu Deng He Zhao Fang Wang Lei Wang |
| author_sort | Jintong Ren |
| collection | DOAJ |
| description | With the successful launch of China’s GF series satellites, it is more important to study the image data quality, the adaptability of processing method and information extraction method. The panchromatic and multi-spectral data which is based on the GF-2 images data of Chinese sub-meter high-resolution remote sensing satellite is fused by PCA, Pansharp, Gram-Schmidt and NNDiffuse fusion. Then, the quality of the fusion images were evaluated subjectively and objectively. In order to evaluate the applicability of different classification algorithms to the classification, the object-oriented classification algorithm which is based on machine learning algorithm, such as KNN, SVM and Random Trees were used to classify the different GF-2 fusion images. The results showed that: (1) The best visual effect of GF-2 fusion image was the Pansharp fusion image; The quantitative evaluation results showed that the brightness and information retention of Gram-Schmidt fusion image was the best，while the Pansharp fusion image had the highest correlation with the original multi-spectral image; the NNDiffuse fusion image had the highest clarity, and the PCA fusion image quantitative evaluation effect was the worst; (2) According to the applicability analysis of the fusion images based on different classification algorithms with features information extraction, it could be seen that the NNDiffuse fusion method was used for the fusion of GF-2 image data, and the classification of the fusion images was more suitable by using KNN or Random Trees classification algorithm. |
| first_indexed | 2024-12-20T16:32:31Z |
| format | Article |
| id | doaj.art-a3b50208dc0d4f2aac501169c24717d7 |
| institution | Directory Open Access Journal |
| issn | 1794-6190 |
| language | English |
| last_indexed | 2024-12-20T16:32:31Z |
| publishDate | 2019-04-01 |
| publisher | Universidad Nacional de Colombia |
| record_format | Article |
| series | Earth Sciences Research Journal |
| spelling | doaj.art-a3b50208dc0d4f2aac501169c24717d72022-12-21T19:33:14ZengUniversidad Nacional de ColombiaEarth Sciences Research Journal1794-61902019-04-0123216316910.15446/esrj.v23n2.8028151283Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification EffectJintong Ren0Wunian Yang1Xin Yang2Xiaoyu Deng3He Zhao4Fang Wang5Lei Wang6Chengdu University of Technology - College of Earth ScienceChengdu University of Technology - College of Earth ScienceChengdu University of Technology - College of Earth ScienceChengdu University of Technology - College of Earth ScienceChengdu University of Technology - College of Earth ScienceChengdu University of Technology - College of Earth ScienceChengdu University of Technology - College of Earth ScienceWith the successful launch of China’s GF series satellites, it is more important to study the image data quality, the adaptability of processing method and information extraction method. The panchromatic and multi-spectral data which is based on the GF-2 images data of Chinese sub-meter high-resolution remote sensing satellite is fused by PCA, Pansharp, Gram-Schmidt and NNDiffuse fusion. Then, the quality of the fusion images were evaluated subjectively and objectively. In order to evaluate the applicability of different classification algorithms to the classification, the object-oriented classification algorithm which is based on machine learning algorithm, such as KNN, SVM and Random Trees were used to classify the different GF-2 fusion images. The results showed that: (1) The best visual effect of GF-2 fusion image was the Pansharp fusion image; The quantitative evaluation results showed that the brightness and information retention of Gram-Schmidt fusion image was the best，while the Pansharp fusion image had the highest correlation with the original multi-spectral image; the NNDiffuse fusion image had the highest clarity, and the PCA fusion image quantitative evaluation effect was the worst; (2) According to the applicability analysis of the fusion images based on different classification algorithms with features information extraction, it could be seen that the NNDiffuse fusion method was used for the fusion of GF-2 image data, and the classification of the fusion images was more suitable by using KNN or Random Trees classification algorithm.https://revistas.unal.edu.co/index.php/esrj/article/view/80281gf-2fusion algorithmobject-oriented classificationclassification effect |
| spellingShingle | Jintong Ren Wunian Yang Xin Yang Xiaoyu Deng He Zhao Fang Wang Lei Wang Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect Earth Sciences Research Journal gf-2 fusion algorithm object-oriented classification classification effect |
| title | Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect |
| title_full | Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect |
| title_fullStr | Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect |
| title_full_unstemmed | Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect |
| title_short | Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect |
| title_sort | optimization of fusion method for gf 2 satellite remote sensing images based on the classification effect |
| topic | gf-2 fusion algorithm object-oriented classification classification effect |
| url | https://revistas.unal.edu.co/index.php/esrj/article/view/80281 |
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