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主成分监督分类及其在水质特征遥感图像识别中的应用 总被引：5，自引：1，他引：4
建立了一种水域水质状况图像识别的主成分监督分类方法,首先通过ＴＭ水域图像数据的主成分分析,将原有各波段图谱的显著且独立的信息集中在数目尽可能少的合成图象中,再依据不同类型水体的光谱特征,分析各主成分图像的构成及其环境生态学含义,由此对整个研究区域内存在的不同标志类型及其分布特征有所了解,在此基础上,选定训练样本集,从而人有清楚的环境生态意义的标志类型,应用监督法得到较好的识别分类结果,分析表明,这 相似文献
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主成分监督分类及其在水质特征遥感图像识别中的应用 总被引：8，自引：0，他引：8
建立了一种水域水质状况图像识别的主成分监督分类方法。首先通过TM水域图像数据的主成分分析，将原有各波段图像的显著且独立的信息集中在数目尽可能少的合成图像中；再依据不同类型水体的光谱特性，分析各主成分图像的构成及其环境生态学含义，由此对整个研究区域内存在的不同标志类型及其分布特征有所了解；在此基础上，选定训练样本集，从而根据具有清楚的环境生态意义的标志类型，应用监督法得到较好的识别分类结果。分析表明，这一方法采用主成分分析确定标志类型，无需大量的现场调查，因而具有非监督聚类成本低的优点，分类结果则优于非监督法，且各类型的生态意义明显，分布特征与环境因子相互吻合，是水域水质环境图像识别的有效而实用的方法。 相似文献
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《地震研究进展（英文）》2021,1(4):100069
In order to improve the accuracy of building structure identification using remote sensing images, a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper. Three identification approaches of remote sensing images are integrated in this method: object-oriented, texture feature, and digital elevation based on DSM and DEM. So RGB threshold classification method is used to classify the identification results. The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed. The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images. 相似文献
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Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion 总被引：3，自引：0，他引：3
HASI Bagan MA Jianwen LI Qiqing HAN Xiuzhen & LIU Zhili Laboratory of Remote Sensing Information Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing China 《中国科学D辑(英文版)》2004,47(7):651-658
Remote sensing classification methods can be classified as supervised and unsupervised catalogs. The maximum likelihood method (MLH) is a super-vised classification method,which is widely used in the remote sensing data classification and produces good results[1]. In the MLH, the parameters are esti-mated, assuming that the samples are normally dis-tributed in spectral space, to determine the mean vec-tor and covariance matrix of all classes. In most cases, however, the samples are not norma… 相似文献
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IINTRODUCTIONLanduse/coverisoneofthemostimportalfactorseffectingsoilandwaterloss.Researchonlanduse/coverandwaterandsoillosswithremotesensinghasbeendonemuchinthepast.Butremotelysensedimageryonlycarriestheinstantaneousandtwo-dimensionalinformationofitsprototypegeographicobjects(ChenandZhao,1990).Thereforemathematicalandphysicalprocessingonremotelysensedimageoftenproduceindefiniteandunreliableresult.Inordertoimprovetheprecisionofclassification,otherdatasetssuchastopographicmaps,thematicmaps… 相似文献
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Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm 总被引：1，自引：0，他引：1
There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery. 相似文献
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选取平顶山平煤矿区附近区域作为研究区,使用无人机进行拍摄,以获悉矿区周边地物种类,补充辅助决策中的居民地、道路等信息,重点研究遥感影像的分析处理。为实现遥感影像分类并增强可视化效果,以无人机影像为源图像,分别采用无人机正射影像的主流处理软件Photoscan和Pix4d,完成图像的预处理及拼接,发现利用Pix4d软件进行图像拼接效果更佳。采用非监督分类及监督分类中的最大似然法和支持向量机,研究影像拼接分类方法,并结合ArcGIS软件,增强可视化效果。通过分析结果图像的类内精度、总体精度、kappa系数指标,完成分类质量评价,发现支持向量机分类效果更佳。此次针对平顶山地区遥感影像的试验结果,对于完善灾害预评估起到一定借鉴作用,并可为震后应急救灾辅助决策提供有效的数据支撑。 相似文献
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湿地植被地上生物量是衡量湿地生态系统健康状况的重要指标,对于珍稀水禽越冬繁殖、全球碳循环、生态净化具有重要意义,是生态学与遥感解译的研究热点之一.针对于地上生物量的测算,卫星遥感数据覆盖范围广但其空间分辨率较低,无人机遥感数据空间分辨率高但采集范围小,同时受湿地面积、观测系统及外界环境等条件的影响,使得遥感影像地上生物量反演更加复杂和困难.本研究基于无人机和高分一号数据对升金湖草滩植被地上生物量反演进行研究,结合升金湖保护区4个样区无人机可见光影像与相应样区实测样本数据,建立地上生物量与可见光波段、多种可见光植被指数的线性、幂函数、多项式、对数回归模型,并通过可决系数（R2）、平均绝对误差（MAE）和均方根误差（RMSE）对模型进行精度评价,选择最优模型对无人机影像进行地上生物量反演;通过可见光波段反演得到的生物量,与高分一号WFV归一化差分植被指数（Normalized Difference Vegetation Index,NDVI）影像相结合进行回归建模,获取整个升金湖草滩植被地上生物量分布.结果表明,利用无人机红光波段建立的多项式方程对地上生物量反演有着最高模拟精度,R2=0.86、预测精度MAE=111.33 g/m2、RMSE=145.42 g/m2,且红光波段生物量反演方法得到的结果与实际生物量分布一致性较高,高分一号WFV NDVI与无人机反演生物量构建的多项式模型为最优模型,R2为0.91.本研究利用无人机和高分一号数据进行生物量反演研究,整合多源遥感数据优点,以获取更加丰富和准确的信息,进而提高地上生物量反演精度,为湿地监测和湿地恢复管理提供数据和技术支撑,具有重要研究意义和应用价值. 相似文献
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遥感分类方法在建筑物震害提取中的应用（以玉树地震为例） 总被引：1，自引：0，他引：1
建筑物损毁情况是地震灾害评估的一项重要指标,利用遥感技术快速提取震后建筑物震害信息,对科学指导地震应急救援工作具有重要意义.利用2010年4月14日青海玉树7.1级地震前后玉树县结古镇团结村高分辨率遥感影像,结合像素光谱和空间特性的纹理、结构等多源信息,基于支持向量机（SVM）方法,对地震前后建筑物信息进行分类提取,变化检测出建筑物损毁情况,并与面向对象多源信息复合的模糊分类法的分类精度、提取效率进行对比分析.研究结果表明,多源数据复合的SVM影像分类方法能够有效解决模糊分类影像破碎问题,地震前后两实相影像分类总精度达到77.53％和73.56％,提高了建筑物震害信息提取精度. 相似文献
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遥感影像识别方法是破坏性地震震后地质灾害快速、准确获取的重要方法之一,传统的遥感影像识别方法主要以人工目视识别方法和半自动识别方法为主,需投入大量的人力和时间。针对破坏性地震震后地质灾害解译时间长、投入人力多等问题,以2017年8月8日四川九寨沟7.0级地震震后高分辨率无人机遥感影像为研究样本,提出基于深度学习网络的地震地质灾害识别方法。首先结合震后遥感影像解译资料和现场调查资料,提取九寨沟地震地质灾害无人机遥感影像特征,并构建研究区地震地质灾害解译指标和分类数据集;然后采用DeepLabv3+网络结构及softmax损失函数,建立基于深度学习网络的地震地质灾害遥感影像图像语义分割模型方法;最后采用半监督学习方法进行结果验证。研究结果表明,基于深度学习网络的地震地质灾害识别方法可有效识别九寨沟地震地质灾害分布信息,整体分类识别准确率为94.22%,F1分数值为0.77,结果具有较好的一致性和准确性,可提升地震现场灾情获取和重点地震隐患识别等工作效率及服务能力。 相似文献
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AnPing Liao LiJun Chen Jun Chen ChaoYing He Xin Cao Jin Chen Shu Peng FangDi Sun Peng Gong 《中国科学:地球科学(英文版)》2014,57(10):2305-2316
Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China’s HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment. 相似文献
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火山灰云不但引起全球气候和环境系统的重大变化,而且还会威胁航空安全。热红外遥感技术为检测火山灰云提供了新手段,但是遥感数据自身的冗余和波段相关性大大降低了火山灰云的检测精度。独立分量分析(Independent Component Analysis,ICA)能够实现遥感数据的去相关和消除冗余,在火山灰云检测中具有一定的潜力。通过探索火山灰云的物理、化学性质,文中以2010年4月19日冰岛艾雅法拉(Eyjafjallajokull)火山灰云MODIS图像为数据源,在对MODIS数据进行主成分分析处理的基础上,利用ICA进行火山灰云检测。结果表明:ICA能够较好地从MODIS图像中获取火山灰云信息,所得结果与美国地质调查局标准光谱数据库和火山灰云SO2浓度分布具有较好的一致性,取得了较好的检测效果。 相似文献
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Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km2) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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面向对象遥感分类方法在汶川地震震害提取中的应用 总被引：7，自引：0，他引：7
震后城市建筑物震害的自动识别与分类, 是遥感震害调查中的关键步骤, 其精度直接影响损失评估的结果. 而随着高分辨率遥感影像的发展, 传统基于像元的分类技术已不能满足需求, 引入面向对象的信息提取技术, 充分挖掘影像对象的纹理、形状和相互关系等信息, 能够有效的提高震害的分类精度. 该文阐述了面向对象的遥感震害提取思路和方法, 并应用汶川地震震后高分辨率航空遥感数据, 针对建筑物震害进行面向对象的快速提取与自动分类. 结果表明, 与基于像元分类比较, 面向对象的建筑物震害分类能够显著改善分类效果. 相似文献
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Ertan Pek?en Türker Yas A. Yekta Kayman Co?kun Özkan 《Journal of Applied Geophysics》2011,75(2):305-318
Particle swarm optimization (PSO) is a global search method, which can be used for quantitative interpretation of self-potential data in geophysics. At the result of this process, parameters of a source model, e.g., the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and regional coefficients are estimated. This study investigates the results and interpretation of a detailed numerical data of some simple body responses, contaminated and field data. The method is applied to three field examples from Turkey and the results are compared with the previous works. The statistics of particle swarm optimization and the corresponding model parameters are analyzed with respect to the number of generation. We also present the oscillations of the model parameters at the vicinity of the low misfit area. Further, we show how the model parameters and absolute frequencies are related to the total number of PSO iterations. Gaussian noise shifts the low misfit area region from the correct parameter values proportional to the level of errors, which directly affects the result of the PSO method. These effects also give some ambiguity of the model parameters. However, the statistical analyses help to decrease these ambiguities in order to find the correct values. Thus, the findings suggest that PSO can be used for quantitative interpretation of self-potential data. 相似文献
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湖冰光谱特征是湖冰遥感反演的物理基础,是研究湖冰光学特性和空间分布的理论依据。本文以查干湖为例,使用ASD Field Spec 4便携式地物光谱仪采集冰封期不同类型湖冰、积雪和水体光谱,利用Savitzky-Golay滤波法和包络线去除法分析白冰、灰冰、黑冰、雪冰、积雪和水体的反射光谱特征,探索气泡对湖冰反射光谱特征的影响。积雪和雪冰、白冰和灰冰、黑冰和水体的反射特征随着波长的变化特征基本一致,冰的反射率介于积雪和水体之间,其中白冰的反射率高于灰冰和黑冰,在包络线去除结果中,黑冰和水体在440 nm吸收谷处的吸收面积为5.184和10.878、吸收深度为0.052和0.106,雪、雪冰、白冰、灰冰在800和1030 nm吸收谷处的吸收面积和吸收深度的变化表现为雪<雪冰<灰冰<白冰。气泡是影响湖冰光谱特征的重要因素,气泡使白冰反射率减小和黑冰反射率增大,并且气泡使得白冰在800/1030nm和黑冰在440 nm处的吸收面积和吸收深度减小,其中气泡大小和疏密程度的不同会导致湖冰反射率的影响程度存在差异。同时,本文选取时间同步的Landsat 8 OLI遥感影像,在完成辐... 相似文献
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基于实测高光谱数据的鄱阳湖湿地植被光谱差异波段提取 总被引：1，自引：0，他引：1
高光谱遥感技术的出现为有效解决湿地植被种类的精细识别和分类提供了可能.通过实地测取鄱阳湖湿地5种植被的高光谱数据,在对数据预处理的基础上,提出一种基于数据误差范围和植被光谱均值差的植被光谱差异波段提取方法.将该方法应用于包络线变换前后的光谱曲线提取植被的光谱差异波段,最后利用马氏距离法检验植被识别效果.结果表明:本文中的方法有效提取了植被光谱差异波段,其中变换前光谱差异波段分别为663~688 nm,变换后为581~636、660~695和1225~1236 nm.在光谱差异波段范围内,同种植被的马氏距离值小于异种植被的马氏距离值,可有效对植被进行识别.研究结果为湿地植被分类识别奠定了理论基础,同时为湖泊湿地植被以及湖泊生态环境的保护决策提供科学依据. 相似文献