http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Dongsheng Ma (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
SAR Image De-noising Based on Residual Image Fusion and Sparse Representation
( Xiaole Ma ),( Shaohai Hu ),( Dongsheng Yang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.7
Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.
-
Haifeng Ding,Dongsheng Ma,Chunyan Yao,Qizhong Lin,Linhai Jing 한국지질과학협의회 2016 Geosciences Journal Vol.20 No.1
The Neoproterozoic successions in the Aksu region, NW China, which lies unconformably on the Precambrian Aksu Group basement, comprises the Qiaoenbrak, Yuermeinak, Sugetbrak, and Chigebrak formations (from bottom to top). The two lowermost units include two distinct glacial diamictites, which indicate distinct episodes of glaciations. We report the major and trace element (including rare earth element) data for the Qiaoenbrak, Yuermeinak, and Sugetbrak formations to identify the paleoclimatic perturbations. The chemical index of alteration (CIA) values show variations from Qiaoenbrak to Yuermeinak, then Sugetbrak formations. The diamictites have relatively lower chemical index of alteration values (45.23–59.64) than inter-, post- and non-glacial sediments (48.28–66.96). This result supported the condition that the diamictites underwent relatively weak chemical weathering from a dry-cold sedimentary environment, which is associated with the sedimentary facies description. The lower Neoproterozoic successions recoded at least two glaciations, one is Qiaoenbrak glaciation and the other is Yuermeinak glaciation.
-
An Effective SVM Ensemble Algorithm Based on Different Thresholds of PCA
Yukai Yao,Bo Wang,Qingjun Yang,Dongsheng Ji,Tao Ma,Xiaoyun Chen 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.2
This paper proposes an effective ensemble classifier, named PCAenSVM, which consists of ten weak Support Vector Machine classifiers based on different Principal Component Analysis thresholds. Those ten base Support Vector Machine classifiers are made up to fulfill classification tasks using Majority Voting strategy. Experiments are made on four UCI data sets and a data set from the Uppsala University to evaluate the performances of PCAenSVM. The results of PCAenSVM are compared with that of LibSVM and EnsembleSVM. Experimental results show that PCAenSVM has better classification accuracy than other two algorithms. Moreover, PCAenSVM has the same confidence level with the LibSVM, and its confidences of accuracy and sensitivity on those five data sets outperform that of the EnsembleSVM.
ScienceON
KISS연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
