共查询到17条相似文献，搜索用时 140 毫秒
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基于遥感影像纹理信息的湖泊围网养殖区提取 总被引：7，自引：3，他引：7
中国东部许多湖泊被人为围网养殖开发,高密度的围网养殖区较容易导致湖泊富营养化和水质恶化,因此人们利用遥感数据开展了湖泊围网养殖区的调查研究．对于湖泊围网养殖区的提取大多采用多光谱分类或目视解译手工数字化,多光谱分类围网养殖区和自然水体易于混淆,而手工数字化对于大区域提取工作量比较大.论文提出了基于遥感影像纹理信息的湖泊围网养殖区提取方法,事实证明此方法易于实现且提取精度高．该文选用中巴资源卫星02星多光谱数据,以白马湖为试验区,首先采用归一化差值植被指数提取水体及少部分光谱特征与之相似的人工建筑物和裸土,再采用主成份分析对研究区影像进行数据压缩和几何信息增强,利用灰度共生矩阵对影像纹理信息进行分析,以均值为量化指标,确定其最佳阈值,最后以决策树分类方法提取湖泊围网养殖区． 相似文献
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基于面向对象的无监督分类的遥感影像自动分类方法 总被引：3，自引：0，他引：3
为了实现无任何先验知识的高分辨率遥感数据的自动分类,并进一步提高自动分类精度和效率,提出了一种基于面向对象的无监督分类方法(Object Oriented Unsupervised Classification).具体步骤如下:首先对遥感影像进行分割,得到一系列空间上相邻、同质性较好的分割单元,然后对分割单元进行特征提取,得到分割单元的对象特征(光谱特征、纹理特征等多特征信息),进而对分割单元进行基于对象特征马氏距离聚类.最后,通过分类后处理(类别合并、错分类别调整等)得到最终的分类结果.通过实验表明:本文提出的方法不仅能够利用影像中更多的特征信息进行聚类而且还可以有效地减少聚类对象的个数,从而使自动分类的精度和效率都得到较大的提升. 相似文献
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研究了长白山天池火山1000年和5000年前2次大喷发产生的浮岩和火山灰的微观特征,通过TEM-EDX和SEM-EDX分析了浮岩中的火山玻璃和长石晶屑的表面特征及风化层厚度。5000年前大喷发浮岩中的火山玻璃风化层平均厚度3·74μm,1000年前大喷发为0·98μm;5000年前大喷发浮岩中的长石表面风化溶蚀度大于1000年前大喷发物中的长石;两次大喷发浮岩中的火山玻璃风化层化学组成与火山玻璃相比富Al和Fe而Si减少。综合上述特征认为,天池火山喷发物中矿物的微观特征的差异与火山喷发年代和喷发环境有关。因此,系统研究天池火山喷发物的微观特征是十分必要的,并且具有一定的应用价值 相似文献
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基于随机森林模型的太湖水生植被遥感信息提取 总被引：3，自引：1，他引：2
水生植被作为太湖湿地的重要组分,其数量和范围变化影响着湖泊生态系统的平衡,故利用遥感技术对水生植被的空间分布开展研究有助于太湖湿地生态系统的保护.以Landsat 8多光谱遥感影像为主要数据源,利用光谱指数和图像变换方法构建多个特征变量,结合随机森林(RF)模型,提取太湖水生植被的空间分布.结果表明:(1)通过对比分析训练样本特征值的平均值、标准差和变异系数,NDVI、NDWIF、SR等指数更易于区分开敞水域和沉水植被、浮叶植被和挺水植被;(2)当设置1000棵分类树和4个分割节点的随机变量时,RF分类模型的袋外误分率小于6%,误分主要受SR、MNDWI和NDVI等特征变量影响;(3)通过验证分析,基于RF模型获得的2014年7月太湖水生植被覆盖面积约为306.0km2,分类精度为88.56%(Kappa系数为0.88),主要分布在湖体的东部和南部,以沉水和浮叶植被为主,两者占水生植被覆盖总面积的84.9%. 相似文献
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基于空间-光谱特征和稀疏表达的高光谱图像分类算法（英文） 总被引：1，自引：0，他引：1
针对传统的高光谱数据分类方法分类精度不高、没有充分地利用空间信息等缺陷,提出一种基于Gabor空间纹理特征（Gabor spatial texture features）及无参数加权光谱特征（Nonparametric weighted spectral features）和稀疏表达分类（Sparse representation classification）的高光谱图像分类算法,可以简写为Gabor-NW SF和SRC,即GNWSF-SRC。所提出的GNWSF-SRC分类方法首先通过融合高光谱的Gabor空间特征和无参数加权光谱特征来更好地描述高光谱图像,然后通过其进行稀疏表达,最终通过对比其重构误差获得分类结果。在训练集比例不同的情况下,用所提出的方法对两组典型的高光谱数据进行处理,理论研究和仿真结果表明：与传统的分类方法相比,所提出算法能够提高分类精度、Kappa系数等,取得了较好的分类效果。 相似文献
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基于无人机倾斜影像的三维建筑物震害精细信息提取 总被引：2，自引：2，他引：0
无人机倾斜摄影技术建模生成的三维影像较好地展现了建筑物侧面和顶面的震害细节信息，然而影像的高维度特性难以直接基于三维影像提取震害信息，经过降低维度转换的二维纹理影像往往会导致建筑物震害信息的不完整性和破碎性。针对这些问题，本文以2017年九寨沟MS7.0地震为例，提出了一种直接从九寨沟震后三维影像获取侧面纹理信息的方法，即将三维模型打散，实现纹理与不规则三角网分离，从而获取完整的纹理影像，然后利用金字塔模型的瓦片坐标范围、瓦片命名规则和建筑物单体的空间位置选取最优纹理影像，再使用加权均值方差法确定纹理影像中建筑物的外墙最佳分割尺度后，采用面向对象方法提取建筑物外墙和墙皮脱落信息，最后通过对这些建筑物震害特征的分析，判定单体建筑物的破坏等级。结果显示，该方法成功获取了建筑物完整的侧面震害纹理影像，并基于纹理影像提取了外墙、裂缝和墙皮脱落区域信息判定建筑物单体为中等、严重两个破坏等级。 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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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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Image classification approaches are widely used in mapping vegetation on remotely sensed images. Vegetation assemblages are equivalent to habitats. Whereas sub-pixel classification approaches potentially can produce more realistic, homogenous habitat maps, pixel-based hard classifier approaches often result in non-homogenous habitat zones. This salt-and-pepper habitat mapping is particularly a challenge on images of savannas, given the characteristic patchy texture of scattered trees and grass. Image segmentation techniques offer possibilities for homogenous habitat classification. This study aimed at establishing the extent to which established, field surveyed and geology-related vegetation types in South Africa’s Kruger National Park (KNP) can be reproduced using image segmentation. Rain season Landsat TM images were used, selected to coincide with the peak in vegetation productivity, which was deemed the time of year when discrimination between key habitats in KNP is most likely to be successful. The multiresolution segmentation mode in eCognition 5.0 was employed, object classification accomplished using the nearest neighbour (NN) classifier, using object texture and training area mean values in the NN feature space.Compared to delineations of the vegetation types of KNP on a digital map of the vegetation zones that was tested, image segmentation successfully mapped the zones (overall accuracy 85.3%, K^ = 82.7%) despite slight shifts in the location of vegetation zone boundaries. Maximum likelihood classification (MLC) of the same images was only 37% accurate (K^ = 24.2%). Whereas the vegetation zones resulting from MLC were non-homogenous, with considerable spectral confusion among the vegetation zones, image segmentation produced more homogenous vegetation zones, comparably more useful for conservation management, because realistic and meaningful habitat maps are important in biodiversity conservation as input data upon which to base management decisions. Image segmentation appears to be a useful approach in mapping savanna vegetation. 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：4，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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Estimating silicic lava vesicularity with thermal remote sensing: a new technique for volcanic mapping and monitoring 总被引：2，自引：0，他引：2
Remote monitoring of active lava domes provides insights into the duration of continued lava extrusion and detection of potentially
associated explosive activity. On inactive flows, variations in surface texture ranging from dense glass to highly vesicular
pumice can be related to emplacement time, volatile content, and internal structure. Pumiceous surface textures also produce
changes in thermal emission spectra that are clearly distinguishable using remote sensing. Spectrally, the textures describe
a continuum consisting of two pure end members, obsidian and vesicles. The distinct spectral features of obsidian are commonly
muted in pumice due to overprinting by the vesicles, which mimic spectrally neutral blackbody emitters. Assuming that this
energy combines linearly in direct proportion to the percentage of vesicles, the surface vesicularity can be estimated by
modeling the pumice spectrum as a linear combination of the glass and blackbody spectra. Based on this discovery, a linear
retrieval model using a least-squares fitting approach was applied to airborne thermal infrared data of the Little Glass Mountain
and Crater Glass rhyolite flows at Medicine Lake Volcano (California) as a case study. The model produced a vesicularity image
of the flow with values from 0 to ∼70%, which can be grouped into three broad textural classes: dense obsidian, finely vesicular
pumice, and coarsely vesicular pumice. Values extracted from the image compare well with those derived from SEM analysis of
collected samples as well as with previously reported results. This technique provides the means to accurately map the areal
distributions of these textures, resulting in significantly different values from those derived using aerial photographs.
If applied to actively deforming domes, this technique will provide volcanologists with an opportunity to monitor dome-wide
degassing and eruptive potential in near-real-time. In July 1999 such an effort will be possible for the first time when repetitive,
global, multispectral thermal infrared data become available with the launch of the Advanced Spaceborne Thermal Emission and
Reflectance Radiometer (ASTER) instrument aboard the Earth Observing System satellite.
Received: 25 June 1998 / Accepted: 14 December 1998 相似文献
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Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams
Remote sensing has rarely been used as a tool to map and monitor submerged aquatic vegetation (SAV) in rivers, due to a combination of insufficient spatial resolution of available image data and strong attenuation of light in water through absorption and scattering. The latter process reduces the possibility to use spectral reflectance information to accurately classify submerged species. However, increasing availability of very high resolution (VHR) image data may enable the use of shape and texture features to help discriminate between species by taking an object based image analysis (OBIA) approach, and overcome some of the present limitations.This study aimed to investigate the possibility of using optical remote sensing for the detection and mapping of SAV. It firstly looked at the possibilities to discriminate submerged macrophyte species based on spectral information only. Reflectance spectra of three macrophyte species were measured in situ across a range of submergence depths. The results showed that water depth will be a limiting factor for the classification of species from remote sensing images. Only Spiked Water Milfoil (Myriophyllum spicatum) was indicated as spectrally distinct through ANOVA analysis, but subsequent Jeffries–Matusita distance analysis did not confirm this. In particular Water Crowfoot (Ranunculus fluitans) and Pondweed (Potamogeton pectinatus) could not be discriminated at 95% significance level. Spectral separability of these two species was also not possible without the effect of an overlying water column.Secondly, the possibility to improve species discrimination, using spatial and textural information was investigated for the same SAV species. VHR image data was acquired with a Near Infrared (NIR) sensitive DSLR camera from four different heights including a telescopic pole and a Helikite UAS. The results show that shape and texture information can improve the detection of the spectrally similar Pondweed and Water Crowfoot from VHR image data. The best performing feature ‘length/width ratio of sub-objects’ was obtained through expert knowledge. All of the shape and texture based features performed better at species differentiation than the spectrally based features.In conclusion this study has shown that there is considerable potential for the combination of VHR data and OBIA to map SAV in shallow stream environments, which can benefit species monitoring and management. 相似文献
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Within areas of salt tectonics, seismic imaging requires extensive updating of the velocity model. This includes defining the boundaries of salt structures which are often characterized by changes in texture of the seismic signal rather than reflectivity. The main characteristics of texture inside salt structures are identified. Three groups of texture attributes are studied: gray-level co-occurrence matrix (GLCM) attributes, frequency-based attributes, and dip and similarity attributes. Various combinations of the selected attributes are tested in a supervised Bayesian classification method. Experimental results show that the classification performance improves by combining at least two texture attribute groups. The classifier computes an estimate of the pixelwise probability of salt. It can then be applied to compute the probability of salt on different seismic sections. Classification results were found more robust based on timeslices. The result from classification, the salt probability image, is then input to a segmentation algorithm that produces a smooth border delimiting the extent of the salt. The segmented salt contours corresponded fairly well to the contours provided by an interpreter. 相似文献
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研究适应信息化时代特征的矿产资源潜力制图新技术、新方法对推动矿产资源评价理论与技术的发展具有重要的意义.笔者把GIS技术、图像分类算法和空间统计学理论进行有机集成,在空间统计学的空间结构分析技术和遥感图像纹理分类算法的基础上,提出了一种以综合地学数据（地质、地球物理、地球化学和遥感图像数据等）为基本数据源的矿产资源潜力自动制图方法.该方法的技术流程为：①数据准备,即对地球物理和地球化学勘探数据进行预处理,生成一个物化遥综合图像文件;②图像空间结构性分析和纹理图像生成,以综合地学图像为研究对象,用空间统计学的结构分析技术研究地学数据综合图像的空间结构性,生成纹理图像;③纹理图像多元分类,用实验变差函数纹理分类方法对研究区进行多元分类,生成分类专题图;④分类后处理,用叠置分析修正空间分类结果,生成区域矿产资源潜力分布图. 相似文献