Feature-Level Fusion of Polarized SAR and Optical Images Based on Random Forest and Conditional Random Fields
שמור ב:
| הוצא לאור ב: | Remote Sensing vol. 13, no. 7 (2021), p. 1323 |
|---|---|
| מחבר ראשי: | |
| מחברים אחרים: | , , , |
| יצא לאור: |
MDPI AG
|
| נושאים: | |
| גישה מקוונת: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| תגים: |
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2550424403 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs13071323 |2 doi | |
| 035 | |a 2550424403 | ||
| 045 | 2 | |b d20210101 |b d20211231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Kong, Yingying |u College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; <email>yanbiyuan@nuaa.edu.cn</email> (B.Y.); <email>liuyanjuan@nuaa.edu.cn</email> (Y.L.) | |
| 245 | 1 | |a Feature-Level Fusion of Polarized SAR and Optical Images Based on Random Forest and Conditional Random Fields | |
| 260 | |b MDPI AG |c 2021 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such as water bodies. One of the current challenges is how to fuse the benefits of both to obtain more powerful classification capabilities. This study proposes a classification model based on random forest with the conditional random fields (CRF) for feature-level fusion classification using features extracted from polarized SAR and optical images. In this paper, feature importance is introduced as a weight in the pairwise potential function of the CRF to improve the correction rate of misclassified points. The results show that the dataset combining the two provides significant improvements in feature identification when compared to the dataset using optical or polarized SAR image features alone. Among the four classification models used, the random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper obtained the best overall accuracy (OA) and Kappa coefficient, validating the effectiveness of the method. | |
| 653 | |a Feature extraction | ||
| 653 | |a Machine learning | ||
| 653 | |a Vegetation | ||
| 653 | |a Accuracy | ||
| 653 | |a Experiments | ||
| 653 | |a Classification | ||
| 653 | |a Synthetic aperture radar | ||
| 653 | |a Support vector machines | ||
| 653 | |a Color | ||
| 653 | |a Decomposition | ||
| 653 | |a Image classification | ||
| 653 | |a Decision trees | ||
| 653 | |a Object recognition | ||
| 653 | |a Land cover | ||
| 653 | |a Datasets | ||
| 653 | |a Conditional random fields | ||
| 653 | |a Remote sensing | ||
| 700 | 1 | |a Biyuan Yan |u College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; <email>yanbiyuan@nuaa.edu.cn</email> (B.Y.); <email>liuyanjuan@nuaa.edu.cn</email> (Y.L.) | |
| 700 | 1 | |a Liu, Yanjuan |u College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; <email>yanbiyuan@nuaa.edu.cn</email> (B.Y.); <email>liuyanjuan@nuaa.edu.cn</email> (Y.L.) | |
| 700 | 1 | |a Leung, Henry |u Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2P 2M5, Canada; <email>Leungh@ucalgary.ca</email> | |
| 700 | 1 | |a Peng, Xiangyang |u Nanjing Research Institute of Electronics Engineering, Nanjing 210007, China; <email>wwukt@163.com</email> | |
| 773 | 0 | |t Remote Sensing |g vol. 13, no. 7 (2021), p. 1323 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2550424403/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2550424403/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2550424403/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |