共查询到17条相似文献，搜索用时 203 毫秒
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在ERDAS软件支持下,对ETM遥感影像数据的TM1—TM5,TM7与其全色波段TM8进行小波变换融合,对融合后的影像采用混合分类方法进行土地利用分类。并对其分类结果进行评价,总体精度达到95.43%,研究结果表明此方法可行有效。 相似文献
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基于TM和SPOT5影像融合的土地利用分类及精度比较——以朝阳区为例 总被引：1，自引：0，他引：1
本文基于TM和SPOT5的影像在ERDASIMAGINE环境中进行融合,在融合前后分别对TM图像与融合后图像进行土地利用分类,对比分类精度并进行定性和定量的解释和分析。本文给出了实验的详细步骤以及完整的评价报告。研究结果表明,融合后图像分类精度有明显提高。对融合后影像进行土地利用分类,在测绘与地图更新、土地利用与城市规划、生态环境监测和政府规划决策等中,具有广阔的应用前景。 相似文献
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《世界地质》2021,(2)
基于光学影像受云雨等不良天气的影响,导致在地物分类时易造成数据信息缺失,而雷达影像作为主动式成像,能够较好地克服这一缺陷。笔者选取长春市净月开发区部分地块为研究区域,分别采用最小距离、最大似然和支持向量机3种分类方法,以Sentinel-1A雷达影像和Sentinel-2A多光谱影像为数据源,基于特征融合,提高地物分类精度。结果表明:特征融合后影像的地物分类精度较单一的光学影像有明显提高,且与最小距离和最大似然相比支持向量机分类精度最高。在无云层覆盖的情况下,融合后支持向量机分类精度达到97.94%,较光学影像提高8.11%;在有云层覆盖情况下,融合后支持向量机精度达到77.29%,较光学影像提高12.5%,尤其对水域和建筑区的识别精度有较大提高。 相似文献
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面向对象的遥感图像分类方法研究 总被引：5，自引：2，他引：3
影响遥感图像分类效果的主要因素之一是空间分辨率。通过融合多分辨率遥感图像,引入面向对象的思想,有效地克服了多光谱图像空间分辨率低的问题。该方法由图像分割和分类等一系列技术组成,首先用基于区域分割法则对正射校正SPOT图像进行分割,然后把它作为参考用最大似然法分类器和其他一些经验规则对TM图像进行分类。对土地覆盖图分类进行精度测试,取得了良好的应用效果。 相似文献
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西昆仑山崇测冰川区多源遥感影像的冰川信息提取方法研究 总被引：2，自引：2，他引：0
冰雪独有的性质与特性使得基于遥感影像对其进行信息提取成为可能,如何进行精准的冰雪信息提取是冰雪时空变化研究的关键和基本要求。利用多源遥感影像（TM、IRS-P5和SAR）对西昆仑山崇测冰川区的冰川进行信息提取,采用不同分类方法和数据融合方法,分别针对光学影像和微波影像进行处理,提取冰川信息并进行比较分析。结果表明：面向对象分类方法是最优的冰川信息提取方法;图像融合处理有助于提高冰川信息的提取精度,特别是多光谱和高分辨率图像融合后再分类,提取效果更为理想。 相似文献
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不同空间分辨率高光谱遥感数据对蚀变矿物信息提取的影响 总被引：1，自引：0，他引：1
高光谱遥感由于其精细的光谱分辨率,在定量分析物质成分上独具优势,因此广泛应用于提取蚀变矿物信息。探讨了不同空间分辨率高光谱遥感数据对蚀变矿物信息提取的影响,采用最邻近插值法、双线性插值法和三次卷积插值法3种重采样方法对美国Cuprite矿区空间分辨率为20 m的AVIRIS影像做空间尺度扩展,分别扩展到空间分辨率为25,30,35,40,45,50 m。采用SAM分类方法从不同空间分辨率影像中提取蚀变矿物信息,使用混淆矩阵评价提取结果。一方面比较不同重采样方法对后期蚀变矿物信息提取精度产生的影响;另一方面比较不同空间分辨率对高光谱遥感影像蚀变矿物信息提取精度的影响。结果表明:①采用不同的重采样方法做空间尺度扩展,会影响后期蚀变矿物信息提取的精度,但是数值变化相对较小。相比之下,最邻近插值法重采样下影像蚀变矿物信息提取的精度稍好一些。②在中等空间分辨率(20～50 m)范围内,基于50 m空间分辨率的高光谱影像,蚀变信息提取的总体精度和Kappa系数较20 m的明显下降。其中最邻近插值法重采样下的总体精度和Kappa系数分别下降了7.94%,0.09;双线性插值法重采样下的总体精度和Kappa系数分别下降了6.87%,0.08;三次卷积插值法重采样下的总体精度和Kappa系数分别下降了6.68%,0.08。较高空间分辨率影像的总体精度和Kappa系数整体上均高于较低空间分辨率的情形。 相似文献
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随着深度学习语义分割的快速发展,基于计算机视觉语义分割模型的高分辨率遥感影像分类方法也大量涌现。为系统定量地研究经典的和先进的视觉语义分割模型在遥感影像分类中的性能,在总结深度学习语义分割进展的基础上,选择9种基于卷积神经网络(CNN)和视觉注意力的语义分割算法,对米级和厘米级2个尺度的遥感数据集进行分析研究。在模型构建上基于计算机视觉通用的语义分割框架,训练时采用红绿蓝3波段遥感图像并基于ImageNet预训练权重进行迁移学习训练。研究结果表明：通用的语义分割模型通过常规训练设置进行训练能取得较好的遥感影像分类效果,部分地物的交并比(IoU)可以达到90%以上;基于视觉注意力的遥感影像分类模型的精度普遍高于基于CNN的模型,且MaskFormer能更有效地提取离散的地物信息;不同类别的精度最高值并不全在总体最优模型中,部分会存在于次优模型中;类似的地物在更高分辨率遥感数据集中可以获得更高的精度。 相似文献
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以高空间分辨率、高光谱分辨率CASI航空遥感数据作为采样带,对黑河中游绿洲灌溉区土地覆盖和农作物种植结构空间格局进行遥感监测。设计了分层分类方法,综合采用基于像素和基于对象的2种遥感图像分类方法对航空样带区域进行土地覆盖制图。根据实地土地覆盖类型调查与目视解译,对样带土地覆盖和农作物种植结构的分类结果进行精度评价,总体分类精度为84.2%,Kappa系数为0.793。与样带区域2007年Landsat TM/ETM+土地覆盖产品相比,高分辨率CASI航空数据能够对树木、草地与农作物类别进行有效监测。监测结果表明,中游绿洲灌溉区内接近59.1%的地区为裸地与建筑用地;植被覆盖区域占39.8%,其中,农田34.9%,树木5.3%,草地仅有0.1%;而在农田区域中玉米为大宗作物,分类成数占96.1%。研究结果表明高质量与高分辨率的航空遥感数据能够实现对流域下垫面异质性进行有效监测,为生态—水文过程研究提供高分辨率的下垫面类型信息。 相似文献
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基于TM影像的抚州地区土地利用/覆盖动态监测及分析 总被引：6，自引：0，他引：6
以1988年和2000年遥感影像为数据源，在ERDAS IMAGINE遥感图像处理软件的支持下，获取了抚州地区两期土地利用／覆盖结果分类图。在此过程中确定了抚州地区土地利用／覆盖分类体系和遥感影像解译标志。通过计算机自动分类和分类后人工解译纠错相结合的图像分类提取方案，提高了图像分类提取的效率和精度。动态监测结果表明，从1988～2000年，面积减少的地类为耕地和林地，面积增加的地类为园地、居民点及工矿用地、水域和未利用土地，并对其变化结果进行了分析。 相似文献
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Estimation of land coverage from a land cover classification derived from remotely sensed data 总被引：1，自引：0，他引：1
Giles M. Foody 《GeoJournal》1995,36(4):361-370
Remotely sensed data are an attractive source of land cover information. In many applications the required information relates to the extent or coverage of land cover class(es) in a region, which is generally derived from a count of the pixels allocated to the class(es) of interest in a classification. A highly accurate classification is not required for the derivation of accurate estimates of class coverage, provided the classification is accompanied by appropriate information on its quality. For instance, the information on classification quality contained in the classification confusion matrix can be used to significantly increase the accuracy of the estimates of land coverage. This is illustrated with reference to a case study focused on the estimation of despoiled land coverage in administratively defined local district in industrial South Wales from Landsat TM data. The accuracy of the investigation was assessed relative to a map of despoiled land cover for this region produced by conventional methods. From an image classification of moderate accuracy, the classification accuracy ranged form 57–83% between the districts investigated, a pixel count provided estimated of despoiled land coverage that were only poorly correlated to the mapped coverage;r = 0.27. Using the information on the pattern of error in the class allocation contained in the classification confusion matrix the estimation accuracy was increased significantly, with a correlation ofr = 0.81 observed between the remote sensing based estimate and the mapped land coverage. Furthermore, the r.m.s. error in despoiled land coverage estimation was reduced by approximately half, to less than 1% district area, when the classification was used in conjunction with information on the pattern of classification error. 相似文献
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基于遥感数据的全球及区域土地覆盖制图--现状、战略和趋势 总被引：21，自引：4，他引：17
土地利用／土地覆盖变化研究是近年来全球变化研究的焦点之一。全球和区域尺度的土地覆盖特征对全球环境状况的评估、模拟未来全球环境的情景有重要的作用。2000年在Internat ionalJournalofRemoteSensing杂志上出版了题为"GlobalandRegionalLandCoverCharacterizat ion from Remotely Sensed Data"的专辑。在此基础上,介绍、总结了国际上利用遥感影像进行全球和区域等大尺度土地覆盖研究的新进展。分别从数据源与制图的时空尺度、制图方法（数据预处理、分类、精度评估）等方面进行了介绍,并对现今的两个全球土地覆盖数据库进行了比较分析。 相似文献
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Saeed Ojaghi Farshid Farnood Ahmadi Hamid Ebadi Raechel Bianchetti 《Arabian Journal of Geosciences》2017,10(21):470
Lake Urmia, located in northwest Iran, contains a number of wetlands significantly affecting the environmental, social, and economic conditions of the region. The ecological condition of Lake Urmia has degraded during the past decade, due to climate change, human activities, and unsustainable management. The poor condition of the lake has also affected the surrounding wetlands. This study analyzes the land cover change of one of the wetlands in the southern part of Lake Urmia, known as Ghara-Gheshlagh wetland, in the period 1989–2015 using post-classification change detection and machine learning image classification. For this analysis, three Landsat images, acquired in 1989 (TM), 2001 (TM), and 2015 (Landsat-8), were used for the classification and change detection. Support vector machine learning algorithm, a supervised learning method, is employed, and images are classified into four main land cover classes namely “water,” ”barren,” “salty land,” and “agriculture and grassland.” Change detection was carried out for pairs of years 1989 to 2001 and 2001 until 2015. The results of this classification show that there is a sharp increase in the area of salt-saturated land as well as a decrease in the area of water resources. Overall classification accuracy obtained were high for the individual years: 1989 (91.48%), 2001 (90.63%), and 2015 (88.6%). Also, the Kappa coefficients for individual maps were high: 1989 (0.89), 2001 (0.8742), and 2015 (0.84). After that, the land cover change map of the study area is obtained between 1989 to 2001 and then 2001 to 2015. The results of this analysis suggest that more efforts should be taken to effectively manage water resources in the region and point to potential locations for focused management actions within the wetland area. 相似文献
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Komeil Rokni Tajul Ariffin Musa Sharifeh Hazini Anuar Ahmad Karim Solaimani 《Natural Hazards》2015,78(1):219-230
The aim of this paper was to investigate the suitability of the pixel-level and product-level image fusion approaches to detect surface water changes. In doing so, firstly, the principal component analysis technique was applied to Landsat TM 2010 multispectral image to generate the PC components. Several pixel-level image fusion techniques were then performed to merge the Landsat ETM+ 2000 panchromatic with the PC1PC2PC3 band combination of Landsat TM 2010 imagery to highlight the surface water changes between the two images. The suitability of the resulting fused images for surface water change detection was evaluated quantitatively and visually. Finally, the support vector machine (SVM) technique was applied to the qualified fused images to map the highlighted changes. Furthermore, a product level fusion (PLF) approach based on various satellite-derived indices was employed to detect the surface water changes between ETM+ 2000 and TM 2010 images. The accuracy of the resulting change maps was assessed based on a reference change map produced using visual interpretation. The results demonstrated the effectiveness of the proposed approaches for surface water change detection, especially using the Gram Schmidt-SVM, PLF-NDWI, and PLF-NDVI methods which improved the accuracy of change detection over 99.70 %. 相似文献