基于极化特征和纹理特征的PolSAR影像建筑物提取方法

极化合成孔径雷达（PolSAR）以其多参数、多通道、多极化、信息记录更加完整等特点，在城市地物提取领域中发挥着重要作用，并已成为遥感影像研究领域的热点。选择覆盖苏州市的Radarsat2影像，利用极化非相干分解法和灰度共生矩阵法分别提取19种极化特征和8种纹理特征，通过分析建筑物、植被和水体的极化特征和纹理特征进行特征组合，结合主成分分析法（PCA）和支持向量机法（SVM）对城市建筑物进行提取，并定量评估精度。结果表明：基于极化特征的建筑物提取精度最高为92.4%；基于纹理特征的提取精度最高为88.9%；极化特征与纹理特征相结合可以提高精度，最高精度为93.7%；PCA特征融合算法具有较高的运算效率，同时提高了精度。     

Ancient Mural Classification Method Based on Improved AlexNet Network

ABSTRACT

As an important part of art and culture, ancient murals depict a variety of different artistic images, and these individual images have important research value. For research purposes, it is often important to first determine the type of objects represented in a painting. However, the mural painting environment makes datasets difficult to collect, and long-term exposure leads to underlying features that are not distinct, which makes this task challenging. This study proposes a convolutional neural network model based on the classic AlexNet network model and combines it with feature fusion to automatically classify ancient mural images. Due to the lack of large-scale mural datasets, the model first expands the dataset by applying image enhancement algorithms such as scaling, brightness conversion, noise addition, and flipping; then, it extracts the underlying features (such as fresco edges) shared by the first stage of a dual channel structure. Subsequently, a second-stage deep abstraction is conducted on the features extracted by the first stage using a two-channel network, each of which has a different structure. The obtained characteristics from both channels are merged, and a loss function is constructed to obtain the classification result. This approach improves the model's robustness and feature expression ability. The model achieves an accuracy of 84.24%, a recall rate of 84.15%, and an F1-measure of 84.13% when applied to a constructed mural image dataset. Compared with the AlexNet model and other improved convolutional neural network models, the proposed model improves each evaluation index by approximately 5%, verifying the rationality and effectiveness of the model for automatic mural image classification. The mural classification model proposed in this paper comprehensively considers the influences of network width and depth and can extract rich details from mural images from multiple local channels. An effective classification method could help researchers manage and protect mural images in an orderly fashion and quickly and effectively search for target images in a digital mural library based on a specified image category, aiding mural condition monitoring and restoration efforts as well as archaeological and art historical research.     

基于语料库的动态词群建构

提出一个以大规模语料库为资源提取材料,以词汇集为语义成分表达方式的动态词群建构方法,其维度特征值的提取不但具有客观性,而且操作简易。维度特征值用词汇集的方式表示特征取值范围,可以避免传统的义素概括性过强、词汇个性表现不足的缺陷。这种方法有利于面向应用的大规模动态词群的建构。     

中国家用电器产品市场扩展特征研究

本文以我国家用电器产品为例，通过采用Logistic模型来描述在产品成长期各产品的市场扩展特征，比较各产品的参数差异，发现其中的规律性，为企业相关产品的市场扩展研究提供科学依据。     

时间——引文分布特征下学者P指数研究

本次研究在P指数基础上，提出了时间——引文分布下学者学术评价P_y指数。具体而言，基于文献引文分布，结合文献被引频次阈值、文献被引半衰期阈值，构建时间——引文分布下的论文分区，即普通论文、潜力论文、热门论文、经典论文。根据4种分区论文影响力差异性，按照普通论文、潜力论文、热门论文、经典论文影响力递增原则，设计论文被引频次递增权值，提出P_y公式。以CNKI平台2000-2017年图情领域核心论文被引频次前100位作者数据进行实证分析，讨论P_y指数表现。结果表明：P_y指数能从论文本身的价值与影响去反映学者的学术成就，凸显优质论文相比普通论文所带来的更强的学术肯定意义；P_y指数区分度、灵敏度、公平公正性、有效性与科学性方面较H指数、P指数表现更佳。     

延边大学图书馆民族文献资源建设现状与发展对策

文章介绍了延边大学图书馆民族文献的收藏和馆藏特色以及存在的一系列问题，并提出了今后图书馆对民族文献建设和发展的具体措施。     

企业联盟绩效私人利益和共同利益评价系统

本首先论述了企业联盟现有的具体评估方法，然后，从企业联盟动力的角度，依据“私人利益（private benefits)”和“共同利益（common benefits)”的分类思想，对企业联盟进行调查问卷，对企业联盟的重要指标进行了综合提取，并使用SPSS的因子建模法进行量化和建模式，使繁多相互交杂的指标评估结果量化直观化，提出，企业联盟绩效P（private benefits)C(common benefits)评价系统，结合具体的案例分析，切实地说明了该系统的两大作用：1）从企业联盟带给企业的私人利益和企业联盟的共同利益出发，从而确定企业联盟中企业的联盟效果和企业联盟的整体效果。（2）通过私人利益和共同利益的比率预测企业联盟的可持续性。     

Delineation of Deep Convective Clouds from GMS/IR Image Using Texture Feature Analysis Method

Monitoring the development and evolution of a deep convec-tive system from gcostationary satellite imagery is very important tonowcasting and heavy rainfall estimation.Using combined infrared andvisible imagery to analyse convective clouds is feasible and effective,but unfortunately,visible satellite data are not available at night.Inthis paper,a method of fuzzy clustering scheme based on texture featureanalysis has been put forward to distinguish the deep convective cloudsfrom cirrus and stratiform by use of GMS/IR image.Results from usingonly IR data and using both IR and VIS data are compared and discus-Sed.     

基于视觉注意计算模型的图像压缩新方法

图像可以被分为重要的感兴趣区域和不重要的背景区域。提出了一种新的基于感兴趣区域的图像压缩方法。该方法首先使用一个基于视觉生理和心理物理实验基础的视觉注意计算模型计算图像中的感兴趣区域,然后用JPEG算法对感兴趣区域和背景区域采用不同的压缩比进行压缩。实验结果表明,该算法具有比JPEG算法更高的压缩效率,同时保证了图像良好的视觉效果。     

安阳市洹河景观规划设计之探析

依托河流所构建的城市,在其景观规划设计中应正确理解城市河流的个性,综合考虑以河流景观开发的相邻区域与构成城市空间的所有要素,把自然、历史和文化特征融合到滨水景观设计环节中,最终把城市设计成一个富有生命的滨水景观城市.