通过软硬变化检测识别冬小麦

提出一种软硬变化检测的作物识别方法 SHLUCD(Soft and Hard Land Use/Cover Change Detection Method)。该方法利用多期遥感影像能够有效表达作物的生长物候特征,以达到在离散变化区(即纯净像元区,包括完全转换成作物的突变区域和非作物区域)和连续变化区(即渐变区,混合像元区,是部分转化为作物的区域)准确进行作物的识别。在北京市选择一个研究区,以冬小麦为研究对象,选用2011年10月6日(播种期)和2012年4月16日(拔节期)两期环境减灾1号卫星影像,分别采用硬变化检测方法 HLUCD(Hard Land Use/Cover Change Detection Method)、软变化检测方法 SLUCD(Soft Land Use/Cover Change Detection Method)和SHLUCD进行冬小麦的识别。实验结果表明:在不同尺度窗口下,SHLUCD较传统方法表现出较明显的优势,具有更低的均方根误差RMSE(SHLUCD为[0.14,0.07],HLUCD为[0.15,0.07],SLUCD为[0.16,0.08])和偏差bias(SHLUCD为-0.0008,HLUCD为-0.007,SLUCD为0.014)和更高的决定系数R2(SHLUCD为[0.68,0.86],HLUCD为[0.62,0.86],SLUCD为[0.60,0.86])。针对冬小麦突变区域、冬小麦渐变区域和非冬小麦区域分别进行评价,表明SHLUCD识别精度接近各区最佳的识别方法,进一步验证了SHLUCD的灵活性和适用性。SHLUCD方法在离散变化区能够通过土地覆盖类型状态变化来有效地识别出冬小麦,在连续变化区可识别出土地覆盖的状态变化程度定量表达冬小麦的丰度,是其他作物多时相遥感变化检测的前期实验基础。     

Land cover change detection in West Jilin using ETM ＋ images

In order to assess the information content and accuracy of Landsat ETM digital images in land cover change detection,change-detection techniques of image differencing, normalized difference vegetation index,principal components analysis and tasseled-cap transformation were applied to yield 13 images. These images were thresholded into change and no change areas. The thresholded images were then checked in terms of various accuracies. The experiment results show that kappa coefficients of the 13 images range from 48.05-78.09. Different images do detect different types of changes. Images associated with changes in the near-inflared-reflectance or greenness detects crop-type changes and changes between vegetative and non-vegetative features. A unique means of using only Landsat imagery without reference data for the assessment of change in arid land are presented. Images of 12t June, 2000 and 2‘d June, 2002 are used to validate the means. Analyses of standard accuracy and spatial agreement are performed to compare the new images (hereafter called “change images” ) representing the change between the two dates. Spatial agreement evaluates the conformity in the classified “change pixels” and “no-change pixels” at the same location on different change images and comprehensively examines the different techniques. This method would enable authorities to monitor land degradation efficiently and aeculately.     

Lignin in marine environment and its analysis—A review

Lignin is a group of phenolic polymers which is abundant in the woody tissues of vascular plants,and is essentially absent from all other living organisms.It has therefore been accepted as a tracer for terrestrial organic carbon(TOC) in marine environment since the 1970s.Lignin polymers are not amenable to direct chemical analysis without prior isolation.This review focused on the methods of chemical decomposition,extraction,derivatization and detection of lignin in marine environment.We described and compared several chemical decomposition methods,including nitrobenzene oxidation,alkaline cupric oxide(CuO) oxidation and thermochemolysis,and detection methods such as gas chromatography(GC),gas chromatography-mass spectrometry(GC-MS),high performance liquid chromatography(HPLC) and so on.Possible improvement of lignin analysis and the application prospects of this tracer were also discussed.     

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北斗三频无几何、消电离层组合周跳探测方法研究

根据多频组合理论,构造基于北斗三频数据的无几何、消电离层组合,结合双频MW组合法和电离层残差法,对北斗实测三频数据进行周跳探测。实验表明,上述3种组合的结合使用能探测出所有大小的周跳,并具有较好的修复效果。     

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基于改进DeepLabv3+孪生网络的遥感影像变化检测方法

传统遥感影像变化检测方法依赖人工构建特征,算法设计复杂且精度不高;而将2幅不同时相影像叠加后输入神经网络的遥感影像变化检测方法会造成不同时相的特征相互影响,难以保持原始影像的高维特征,且模型鲁棒性较差。因此,本文提出一种基于改进DeepLabv3+孪生网络的遥感影像变化检测方法,以经典DeepLabv3+网络的编解码结构为基础对网络进行改进:(1)在编码阶段利用共享权值的孪生网络提取特征,通过2个输入端分别接收2幅遥感影像,以保留不同时相影像的高维特征;(2)在特征融合中用密集空洞空间金字塔池化模型代替空洞空间金字塔池化模型,通过密集连接的方式结合每个空洞卷积的输出,以提高对不同尺度目标分割的精度;(3)在解码阶段中针对不同层级特征图信息差异较大,难以融合的问题,引入基于注意力机制的特征对齐模型,引导不同层级的特征对齐并强化学习重要特征,以提升模型的鲁棒性。应用开源数据集CDD验证本文方法的有效性,并与UNet-EF、FC-Siam-conc、Siam-DeepLabv3+和N-Siam-DeepLabv3+网络对比试验。试验结果表明,本文方法在精确率、召回率、F1值和总体精度上达到8...     

浅层地震反射在郧西盆地断层探测中的应用与研究

在前期详细地质调查的基础上,对郧西盆地进行针对性的浅层地震反射勘探,并利用地震发射结果来确定断层位置及构造特征。该研究对南水北调中线核心水源区地震安全系统的建设有一定意义。     

科学合作地域倾向性研究——以中国雾霾研究为例

科学合作是促进知识传播和共享的重要途径,已有研究表明,地理因素是影响科学合作的主要因素之一。然而,目前针对该问题的研究大多只是从科学计量学的角度,对科学合作强度与地理距离的函数关系进行描述,无法揭示科学合作在空间上的分布特征和内部差异性。因此,本文从地理学的角度,以中国雾霾研究的合作网络为例,通过对文献题录中的位置信息进行解析,将虚拟的科学合作网络映射到地理合作网络。在此基础上,提出了一种考虑地理距离的科学合作网络社区发现方法,挖掘科学合作网络中蕴含的空间聚类特征,从而对科学合作的地域倾向性进行反映。通过比较发现,基于合作频次与地理距离的社区发现算法,能够使社区内部的平均地理距离最小而合作强度最大,既反映了科学合作在地理上的近似性,又体现了科学合作强度特征。该方法能够直观地揭示科学合作中隐含的空间分布模式和联系,对其他复杂网络的地理社区划分也有一定的借鉴意义。