共查询到20条相似文献，搜索用时 125 毫秒
1.
获取具有时态特性的NDVI曲线是进行土地利用与植被覆盖变化分析的必要步骤,为有效地利用多源遥感影像数据,本文基于尺度下降理论,利用具有不同时间分辨率的高、低空间分辨率遥感影像,采用线性光谱混合模型反向分解低空间分辨率混合像元,计算其子像元级地物反射率,生成具有高时态特性的子像元级NDVI时间序列曲线,使利用有限的遥感数据资源进行较精细的动态植被生物量变化分析成为可能。通过真实影像数据实验分析,其结果验证了该方法的有效性。 相似文献
2.
高光谱遥感图像光谱分辨率高、波谱连续、图谱合一,这为精细地物分类、探测和识别提供了数据基础。然而,由于高光谱遥感图像空间分辨率的局限性及地物场景的复杂分布,混合像元普遍存在于高光谱遥感图像。混合像元是高光谱遥感图像精细信息提取与分析中的难点。解决混合像元问题,实现亚像元级信息的提取与分析是近年来高光谱遥感图像解译的热点和前沿。本文系统梳理了高光谱遥感图像亚像元信息提取的主要研究内容,具体从混合像元分解、亚像元制图及亚像元目标探测3个研究方向综述了经典方法,并对国内外相关方向的研究进展、发展前沿及主要挑战进行了分析与评价,最后分析讨论了高光谱遥感图像亚像元信息提取研究在模型构建、优化求解及与应用结合等方面的研究趋势及方向。 相似文献
3.
结合Sentinel-2影像及其他高分辨率卫星数据进行长序列、高频次、大范围的水面率、蓄水量、生态流量等水资源要素监测具有重要意义。为了提高水体提取精度,解决利用多源中高分辨率卫星数据提取水体时的空间尺度效应问题,本文提出了一种面向Sentinel-2影像的亚像元级水体提取方法(简称SWES)。首先利用RWI提取纯水体像元,然后利用膨胀算法提取水陆边界混合像元,最后为解决地物的类内光谱变化问题,采用考虑空间信息的多端元光谱混合分析算法(MESMA)求解水陆混合像元中的水体丰度。3个试验区的结果均表明,SWES取得了较好效果,平均RMSE为0.147,水体提取效果均优于自动亚像元水体提取方法(简称ASWM),尤其在水陆混合像元较多的坑塘养殖区。SWES在试验区获取的水体面积也有较高精度,平均相对误差为8.03%,低于ASWM的20.23%,结果表明SWES能够有效提升水域面积提取精度。 相似文献
4.
《地理空间信息》2017,(2)
传统的植被状况调查方式费时、费力,并且更新困难,而高光谱遥感数据图谱合一,能够更精细、准确地进行遥感地物识别和分类,因此采用Hyperion高光谱数据来研究地物混合严重并且呈零星碎片状的城市植被。利用混合像元分解思想改进Gram-Schmidt融合算法,将Hyperion高光谱和ALI全色波段进行融合,提高光谱数据的空间分辨率,来解决城市植被像元混合严重和分布过于零散破碎难题,进而提高植被识别精度。为了避免高光谱植被识别陷入维数灾难,采用主成分分析对融合后的高光谱数据进行数据降维。最后,在地面光谱成像仪获取的纯净像元光谱信息辅助下,选取训练样本进行最小距离分类,完成植被类型识别,总体精度达到84.9%。 相似文献
5.
高光谱遥感影像混合像元分解研究进展 总被引：6，自引：1，他引：5
受高光谱成像仪低空间分辨率及复杂地物的影响,高光谱遥感图像存在大量混合像元。为提高地表分类精度以及满足亚像元级目标探测的需求,混合像元分解技术一直是高光谱遥感研究热点之一。本文主要对高光谱混合像元分解技术中的核心问题:端元数目估计、端元提取算法、丰度估计算法进行综述,系统地分析了各种典型算法的原理及优缺点,进一步阐述研究过程中建立高精度遥感混合反演模型与遥感产品业务化中的混合像元分解技术难题,同时针对今后混合像元分解技术发展方向,指出在继续引入新型算法理论方法基础上,结合用户应用需求,推进高光谱混合像元分解算法业务化应用,为高光谱遥感工程化应用提供支持。 相似文献
6.
为了满足水文和气象模型对长时段积雪面积数据的需求,基于第二代甚高分辨率辐射计（second series of advanced very high resolution radiometer,AVHRR/2）的10 d合成数据提出了一种青藏高原地区AVHRR/2数据亚像元雪填图算法,将中分辨率遥感数据亚像元级积雪面积数据集延伸至30 a时间跨度。本文算法以多端元线性光谱混合分析模型为基础,以归一化植被指数、第一波段、第二波段等作为选取端元的指标,直接从AVHRR/2图像中自动选取所需雪端元与非雪端元。基于TM数据对该算法的AVHRR/2数据亚像元雪填图结果进行验证,其均方根误差接近0.1,在青藏高原山区具有较高的精度。 相似文献
7.
在对太湖、巢湖等大型湖泊进行业务化蓝藻水华遥感监测工作中,常以250 m空间分辨率的MODIS数据为主,但其像元多为水体和水华的混合像元,若用常规方法进行水华面积提取,势必会严重影响水华监测的精度和实际应用效果。针对上述问题,基于混合像元分解原理,通过混合像元分解得到水华组分在混合像元中的丰度(百分比),实现亚像元级的水华面积提取。该方法可直接根据图像的DN值进行水华面积提取,无需对数据进行辐射校正和大气校正等预处理。与常规水华提取法相比,该方法的水华面积提取精度提高了近30%。 相似文献
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国家公园的土地覆盖分类对于掌握自然资源现状、查明存在的生态安全威胁并快速应对具有基础性数据支撑作用。基于谷歌地球引擎（Google Earth Engine,GEE）平台,结合哨兵（Sentinel）主被动遥感数据及其导出的光谱指数、纹理特征和地形特征,分别采用基于像元的随机森林（random forest,RF）算法和面向对象的简单非迭代聚类（simple noniterative clustering,SNIC）+RF算法实现了钱江源国家公园异质性景观的土地覆盖（耕地、森林、草地、水体、人造地表和裸地）分类。地面实验表明,在多种输入数据组合中,基于像元和面向对象方法分类获得的最高总体精度分别为92.37%和93.98%。合成孔径雷达（synthetic aperture radar,SAR）数据的纳入能够提高基于像元方法的分类精度,但在面向对象方法中未能体现精度提升效果。通过SNIC+RF算法生成的土地覆盖分类图完整性更好,所需特征数量较少,并且算法能够在GEE环境下快速执行,适用于国家公园管理实践。 相似文献
10.
提出了一种基于Landsat TM的地表温度二次像元分解方法,将地表温度的空间分辨率从120 m提高到30 m。首先,利用地表类型的线性统计模型（E-DisTrad）获取初次分解子像元的地表温度,计算得到初次分解子像元的辐亮度;然后,利用面向对象的图像分割方法获取二次分解子像元的权重,实现对地表温度的二次分解;最后,采用升尺度再分解的验证方法进行精度分析,并选取了北京市TM影像进行实例分析。实验结果表明,二次像元分解模型不仅能有效地提高地表温度的空间分辨率,反映出不同地表类型地表温度的空间差异性,而且保证了像元分解前后能量值的一致性,非常适合于复杂地表覆盖地区的热红外波段遥感影像数据的降尺度处理。 相似文献
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Cenk Donmez Suha Berberoglu Paul J. Curran 《International Journal of Applied Earth Observation and Geoinformation》2011
The aim of this study is to use full spatial resolution Envisat MERIS data to drive an ecosystem productivity model for pine forests along the Mediterranean coast of Turkey. The Carnegie, Ames, Stanford Approach (CASA) terrestrial biogeochemical model, designed to simulate the terrestrial carbon cycle using satellite sensor and meteorological data, was used to estimate annual regional fluxes in terrestrial net primary productivity (NPP). At its core this model is based on light-use efficiency, influenced by temperature, rainfall and solar radiation. Present climate data was generated from 50 climate stations within the watershed using co-kriging. Regional scale pseudo-warming data for year 2070 were derived using a Regional Climate Model (RCM) these data were used to downscale the GCM General Circulation Model for the research area as part of an international research project called Impact of Climate Changes on Agricultural Production Systems in Arid Areas (ICCAP). Outputs of climate data can be moderated using the four variables of percent tree cover, land cover, soil texture and NDVI. This study employed 47 MERIS images recorded between March 2003 and September 2005 to derive percent tree cover, land cover and NDVI. Envisat MERIS data hold great potential for estimating NPP with the CASA model because of the appropriateness of both its spatial and its spectral resolution. 相似文献
13.
Dengsheng Lu Emilio Moran Scott Hetrick 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(3):298-306
Mapping and monitoring impervious surface dynamic change in a complex urban-rural frontier with medium or coarse spatial resolution images is a challenge due to the mixed pixel problem and the spectral confusion between impervious surfaces and other non-vegetation land covers. This research selected Lucas do Rio Verde County in Mato Grosso State, Brazil as a case study to improve impervious surface estimation performance by the integrated use of Landsat and QuickBird images and to monitor impervious surface change by analyzing the normalized multitemporal Landsat-derived fractional impervious surfaces. This research demonstrates the importance of two-step calibrations. The first step is to calibrate the Landsat-derived fraction impervious surface values through the established regression model based on the QuickBird-derived impervious surface image in 2008. The second step is to conduct the normalization between the calibrated 2008 impervious surface image with other dates of impervious surface images. This research indicates that the per-pixel based method overestimates the impervious surface area in the urban-rural frontier by 50%-60%. In order to accurately estimate impervious surface area, it is necessary to map the fractional impervious surface image and further calibrate the estimates with high spatial resolution images. Also normalization of the multitemporal fractional impervious surface images is needed to reduce the impacts from different environmental conditions, in order to effectively detect the impervious surface dynamic change in a complex urban-rural frontier. The procedure developed in this paper for mapping and monitoring impervious surface area is especially valuable in urban-rural frontiers where multitemporal Landsat images are difficult to be used for accurately extracting impervious surface features based on traditional per-pixel based classification methods as they cannot effectively handle the mixed pixel problem. 相似文献
14.
黄河口遥感图像光谱混合分解 总被引：6，自引：0，他引：6
探讨了用逻辑斯蒂法进行了光谱混合分解的新技术，采用黄河口LM图像进行了分析。结果表明，它不仅能给出分类结果图像，而且能产生组成像元各地类的丰度图像，说明分类图像是在某种置信度下的结果。 相似文献
15.
Landscape patterns in a region have different sizes, shapes and spatial arrangements, which contribute to the spatial heterogeneity of the landscape and are linked to the distinct behavior of thermal environments. There is a lack of research generating landscape metrics from discretized percent impervious surface area data (ISA), which can be used as an indicator of urban spatial structure and level of development, and quantitatively characterizing the spatial patterns of landscapes and land surface temperatures (LST). In this study, linear spectral mixture analysis (LSMA) is used to derive sub-pixel ISA. Continuous fractional cover thresholds are used to discretize percent ISA into different categories related to urban land cover patterns. Landscape metrics are calculated based on different ISA categories and used to quantify urban landscape patterns and LST configurations. The characteristics of LST and percent ISA are quantified by landscape metrics such as indices of patch density, aggregation, connectedness, shape and shape complexity. The urban thermal intensity is also analyzed based on percent ISA. The results indicate that landscape metrics are sensitive to the variation of pixel values of fractional ISA, and the integration of LST, LSMA. Landscape metrics provide a quantitative method for describing the spatial distribution and seasonal variation in urban thermal patterns in response to associated urban land cover patterns. 相似文献
16.
Successful retrieval of urban impervious surface area is achieved with remote sensing data using the multiple endmember spectral mixture analysis (MESMA). MESMA is well suited for studying the urban impervious surface area because it allows the number and types of the endmembers to vary on a per-pixel basis, thereby, allowing the control of the large spectral variability. However, MESMA must calculate all potential endmember combinations of each pixel to determine the best-fit one. Therefore, it is a time-consuming and inefficient unmixing technology, especially for hyperspectral images because these images have more complicated endmember categories. Hence, in this paper, we design an improved MESMA (SASD-MESMA: spectral angle and spectral distance MESMA) to enhance the computational efficiency of conventional MESMA, and we validate this new method by analyzing the Hyperion image (Jan-2011) and the field-spectra data of Guangzhou (China). In SASD-MESMA, the parameters of spectral angle (SA) and spectral distance (SD) are used to evaluate the similarity degree between library spectra and image spectra in order to identify the most representative endmember combination for each pixel. Results demonstrate that the SA and SD parameters are useful to reduce misjudgment in selecting candidate endmembers and effective for determining the appropriate endmembers in one pixel. Meanwhile, this research indicates that the proposed SASD-MESMA performs very well in retrieving impervious surface area, forest, grass and soil distributions on the sub-pixel level (the overall root mean square error (RMSE) is 0.15 and the correlation coefficient of determination (R2) is 0.68). 相似文献
17.
The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters. 相似文献
18.
Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis 总被引：3，自引：0，他引：3
Gautam Ghosh Suresh Kumar S. K. Saha 《Journal of the Indian Society of Remote Sensing》2012,40(1):129-136
Development of salt-affected soils in the irrigated lands of arid and semi-arid region is major cause of land degradation.
Hyperion hyperspectral remote sensing data (EO-1) was used in the present study for characterization and mapping of salt-affected
soils in a part of irrigation command area of Indo-Gangetic alluvial plains. Linear spectral mixture analysis approach was
used to map various categories of salt affected soils represented by spectral endmembers of slightly, moderately and highly
salt-affected soils. These endmembers were related to surface expression of various categories of salt-affected soils in the
area. The endmembers were selected by performing minimum noise fraction (MNF) transformation and pixel purity index (PPI)
on Hyperion (EO-1) data with reference to high resolution LISS IV data and field data. The results showed that various severity
classes of salt-affected soils could be reliably mapped using linear spectral unmixing analysis. A low RMSE value (0.0193)
over the image was obtained that revealed a good fit of the model in identification and classification of endmembers of various
severities of salt affected soils. The overall classification accuracies for slight, moderate and highly salt-affected soils
were estimated of 78.57, 79.81 and 84.43% respectively. 相似文献
19.
中低分辨率遥感影像中广泛存在的混合像元极大地限制了变化检测结果的精度。基于混合像元分解技术，能够深入到像元内部，比较不同端元的组成分差异影像，然后获取亚像元级别的变化信息。如何从差异影像中确定合适的变化阈值，从而准确地判断变化是否发生，是一个难点问题。在高斯模型分布假设的情况下，采用最大期望法（expectation maximization，EM）自动提取最佳阈值，完成自适应的变化检测过程。选择了两种典型的阈值选择方法与该方法进行比较，结果证明基于EM的自适应变化检测方法可以更准确地提取变化信息，具有较好的稳健性。 相似文献
20.
《International Journal of Digital Earth》2013,6(3):194-216
Information on Earth's land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors. In this study, we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery. For this purpose, the spectral angle mapper (SAM), the object-based and the non-linear spectral unmixing based on artificial neural networks (ANNs) techniques were applied. A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification, namely of the pixel purity index (PPI) and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites. Object-based classification outperformed the other techniques with an overall accuracy of 83%. Sub-pixel classification based on the ANN showed an overall accuracy of 52%, very close to that of SAM (48%). SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%. Yet, all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery, which affected the spectral separation among the land use/cover classes. 相似文献