A novel multi-parameter support vector machine for image classification
文献类型：期刊论文
| ; | |
| 作者 | C. Zhang; T. J. Wang; P. M. Atkinson; X. Pan; H. P. Li |
| 刊名 | International Journal of Remote Sensing
 ; International Journal of Remote Sensing
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| 出版日期 | 2015 ; 2015 |
| 卷号 | 36期号:7页码:1890-1906 |
| 中文摘要 | The support vector machine (SVM) classification algorithm has received increasing attention in recent years in remote sensing for land-cover classification. However, it is well known that the performance of the SVM is sensitive to the choice of parameter settings. The traditional single optimized parameter SVM (SOP-SVM) attempts to identify globally optimized parameters for multi-class land-cover classification. In this article, a novel multi-parameter SVM (MP-SVM) algorithm is proposed for image classification. It divides the training set into several subsets, which are subsequently combined. Based on these combinations, sub-classifiers are constructed using their own optimum parameters, providing votes for each pixel with which to construct the final output. The SOP-SVM and MP-SVM were tested on three pilot study sites with very high, high, and low levels of landscape complexity within the Sanjiang Plain - a typical inland wetland and freshwater ecosystem in northeast China. A high overall accuracy of 82.19% with kappa coefficient (kappa) of 0.80 was achieved by the MP-SVM in the very high-complexity landscape, statistically significantly different (z-value = 3.77) from the overall accuracy of 72.50% and kappa of 0.69 produced by the traditional SOP-SVM. Besides, for the moderate-complexity landscape a significant increase in accuracy was achieved (z-value = 2.44), with overall accuracy of 84.03% and kappa of 0.80 compared with an overall accuracy 76.05% and kappa of 0.71 for the SOP-SVM. However, for the low-complexity landscape the MP-SVM was not significantly different from the SOP-SVM (z-value = 0.80). Thus, the results suggest that the MP-SVM method is promising for application to very high and high levels of landscape complexity, differentiating complex land-cover classes that are spectrally mixed, such as marsh, bare land, and meadow.; The support vector machine (SVM) classification algorithm has received increasing attention in recent years in remote sensing for land-cover classification. However, it is well known that the performance of the SVM is sensitive to the choice of parameter settings. The traditional single optimized parameter SVM (SOP-SVM) attempts to identify globally optimized parameters for multi-class land-cover classification. In this article, a novel multi-parameter SVM (MP-SVM) algorithm is proposed for image classification. It divides the training set into several subsets, which are subsequently combined. Based on these combinations, sub-classifiers are constructed using their own optimum parameters, providing votes for each pixel with which to construct the final output. The SOP-SVM and MP-SVM were tested on three pilot study sites with very high, high, and low levels of landscape complexity within the Sanjiang Plain - a typical inland wetland and freshwater ecosystem in northeast China. A high overall accuracy of 82.19% with kappa coefficient (kappa) of 0.80 was achieved by the MP-SVM in the very high-complexity landscape, statistically significantly different (z-value = 3.77) from the overall accuracy of 72.50% and kappa of 0.69 produced by the traditional SOP-SVM. Besides, for the moderate-complexity landscape a significant increase in accuracy was achieved (z-value = 2.44), with overall accuracy of 84.03% and kappa of 0.80 compared with an overall accuracy 76.05% and kappa of 0.71 for the SOP-SVM. However, for the low-complexity landscape the MP-SVM was not significantly different from the SOP-SVM (z-value = 0.80). Thus, the results suggest that the MP-SVM method is promising for application to very high and high levels of landscape complexity, differentiating complex land-cover classes that are spectrally mixed, such as marsh, bare land, and meadow. |
| WOS记录号 | WOS:000353576900008 |
| 源URL | [http://159.226.123.10/handle/131322/6517]  |
| 专题 | 东北地理与农业生态研究所_合作研究组 |
| 推荐引用方式 GB/T 7714 | C. Zhang,T. J. Wang,P. M. Atkinson,et al. A novel multi-parameter support vector machine for image classification, A novel multi-parameter support vector machine for image classification[J]. International Journal of Remote Sensing, International Journal of Remote Sensing,2015, 2015,36, 36(7):1890-1906, 1890-1906. |
| APA | C. Zhang,T. J. Wang,P. M. Atkinson,X. Pan,&H. P. Li.(2015).A novel multi-parameter support vector machine for image classification.International Journal of Remote Sensing,36(7),1890-1906. |
| MLA | C. Zhang,et al."A novel multi-parameter support vector machine for image classification".International Journal of Remote Sensing 36.7(2015):1890-1906. |
入库方式： OAI收割
来源：东北地理与农业生态研究所
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