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Hyperspectral Image Classification by Fusion of Multiple Classifiers
Yanbin Peng,Zhigang Pan,Zhijun Zheng,Xiaoyong Li 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.2
Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue. Firstly, to solve the problem of computational complexity, spectral clustering algorithm is imported to select efficient bands for subsequent classification task. Secondly, due to lack of labeled training sample points, this paper proposes a new algorithm that combines support vector machines and Bayesian classifier to create a discriminative/generative hyperspectral image classification method using the selected features. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.
Manifold Sparse Coding Based Hyperspectral Image Classification
Yanbin Peng,Zhijun Zheng,Jiming Li,Zhigang Pan,Xiaoyong Li,Zhinian Zhai 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.12
Hyperspectral image classification has received an increasing amount of interest in recent years. However, when representing pixels as vectors, the dimensionality of feature space is high, which causes “curse of dimensionality” problem. In this paper, in order to alleviate the impact of above problem, a manifold sparse coding method is proposed. Firstly, matrix decomposition technique is used to find a concept set and calculates relative data projection in the concept set. Secondly, manifold learning regularization is imported into objective function to capture the intrinsic geometric structure in the data. Finally, LASSO regularization is used to obtain sparse representation of data projection. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.
Hyperspectral Image Classification based on Co-training
Zhijun Zheng,Yanbin Peng 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.12
The abundant information available in hyperspectral image has provided important opportunities for land-cover classification and recognition. However, “Curse of dimensionality” and small training sample set are two difficulties which hinder the improvement of computational efficiency and classification precision. In this paper, we present a co-training based method on hyperspectral image classification. Firstly, two views of samples are generated through two kinds of dimensionality reduction methods. After that, the co-training process is viewed as combinative label propagation over two independent views. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.
Yan Bin,Li Xurui,Peng Mou,Zuo Yali,Wang Yinhuai,Liu Pian,Ren Weigang,Jinxin Liu 생화학분자생물학회 2023 Experimental and molecular medicine Vol.55 No.-
Aberrant glucose metabolism is a characteristic of bladder cancer. Hyperglycemia contributes to the development and progression of bladder cancer. However, the underlying mechanism by which hyperglycemia promotes the aggressiveness of cancers, especially bladder cancer, is still incompletely understood. N6-methyladenosine (m6A) modification is a kind of methylation modification occurring at the N6 position of adenosine that is important for the pathogenesis of urological tumors. Recently, it was found that the m6A reader YTHDC1 is regulated by high-glucose conditions. In our study, we revealed that YTHDC1 is not only regulated by high-glucose conditions but is also downregulated in bladder cancer tissue and associated with the prognosis of cancer. We also showed that YTHDC1 suppresses the malignant progression of and the glycolytic process in bladder cancer cells in an m6A-dependent manner and determined that this effect is partially mediated by GLUT3. Moreover, GLUT3 was found to destabilize YTHDC1 by upregulating RNF183 expression. In summary, we identified a novel YTHDC1/GLUT3/RNF183 feedback loop that regulates disease progression and glucose metabolism in bladder cancer. Collectively, this study provides new insight regarding the pathogenesis of bladder cancer under hyperglycemic conditions and might reveal ideal candidates for the development of drugs for bladder cancer.