[Objective] The purpose of this study is to judge the rationality of land use structure from new perspectives and method. [Method] The theory of data en- velopment analysis （DEA） has been employed in this article. A...[Objective] The purpose of this study is to judge the rationality of land use structure from new perspectives and method. [Method] The theory of data en- velopment analysis （DEA） has been employed in this article. A case study of evalu- ation of land use structure for Wujin district in 2012 is provided to illustrate the ap- plication of this research model. At the micro perspective, land use efficiency has been analyzed by use of data envelopment analysis model. The optimization and adjustment direction in land use structure has been pointed out from the view point of land use efficiency. [Result] The result has shown that the research method based on information entropy and data envelopment analysis model can effectively overcome the drawback of traditional evaluation techniques. Compared with the tra- ditional methods, the new optimization model based on a structure of the multi-crite- ria factors and objective weighting method can evaluate the rationality of land use structure more comprehensively.展开更多
Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remain...Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remains an open problem,which may hinder further development of enhancement techniques.In this paper,a no-reference quality metric for digitally enhanced images is proposed.Three kinds of features are extracted for characterizing the quality of enhanced images,including non-structural information,sharpness and naturalness.Specifically,a total of 42 perceptual features are extracted and used to train a support vector regression(SVR) model.Finally,the trained SVR model is used for predicting the quality of enhanced images.The performance of the proposed method is evaluated on several enhancement-related databases,including a new enhanced image database built by the authors.The experimental results demonstrate the efficiency and advantage of the proposed metric.展开更多
文摘[Objective] The purpose of this study is to judge the rationality of land use structure from new perspectives and method. [Method] The theory of data en- velopment analysis （DEA） has been employed in this article. A case study of evalu- ation of land use structure for Wujin district in 2012 is provided to illustrate the ap- plication of this research model. At the micro perspective, land use efficiency has been analyzed by use of data envelopment analysis model. The optimization and adjustment direction in land use structure has been pointed out from the view point of land use efficiency. [Result] The result has shown that the research method based on information entropy and data envelopment analysis model can effectively overcome the drawback of traditional evaluation techniques. Compared with the tra- ditional methods, the new optimization model based on a structure of the multi-crite- ria factors and objective weighting method can evaluate the rationality of land use structure more comprehensively.
基金supported in part by the National Natural Science Foundation of China under Grant 61379143in part by the Fundamental Research Funds for the Central Universities under Grant 2015QNA66in part by the Qing Lan Project of Jiangsu Province
文摘Image enhancement is a popular technique,which is widely used to improve the visual quality of images.While image enhancement has been extensively investigated,the relevant quality assessment of enhanced images remains an open problem,which may hinder further development of enhancement techniques.In this paper,a no-reference quality metric for digitally enhanced images is proposed.Three kinds of features are extracted for characterizing the quality of enhanced images,including non-structural information,sharpness and naturalness.Specifically,a total of 42 perceptual features are extracted and used to train a support vector regression(SVR) model.Finally,the trained SVR model is used for predicting the quality of enhanced images.The performance of the proposed method is evaluated on several enhancement-related databases,including a new enhanced image database built by the authors.The experimental results demonstrate the efficiency and advantage of the proposed metric.
