排序方式： 共有54条查询结果，搜索用时 31 毫秒
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Dengsheng Lu Guiying Li Emilio Moran Mateus Batistella Corina C. Freitas 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(6):798-808
This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban–rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method. 相似文献
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极化干涉相干矩阵服从复Wishart分布,通过对相关系数的分析可以获得不同的地物类别。在总结极化干涉非监督Wishart ML分类流程的基础上,基于该方法对塔河地区全极化PALSAR数据进行了分类,研究结果表明:基于极化干涉的分类方法能够有效区分不同散射机制对应的地物,该分类方法具有较强的适应性,并且类间边界比较明显,这些分类信息为森林资源的开发和利用提供了参考。 相似文献
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Forests are important biomes covering a major part of the vegetation on the Earth, and as such account for seventy percent of the carbon present in living beings. The value of a forest’s above ground biomass (AGB) is considered as an important parameter for the estimation of global carbon content. In the present study, the quad-pol ALOS-PALSAR data was used for the estimation of AGB for the Dudhwa National Park, India. For this purpose, polarimetric decomposition components and an Extended Water Cloud Model (EWCM) were used. The PolSAR data orientation angle shifts were compensated for before the polarimetric decomposition. The scattering components obtained from the polarimetric decomposition were used in the Water Cloud Model (WCM). The WCM was extended for higher order interactions like double bounce scattering. The parameters of the EWCM were retrieved using the field measurements and the decomposition components. Finally, the relationship between the estimated AGB and measured AGB was assessed. The coefficient of determination (R2) and root mean square error (RMSE) were 0.4341 and 119 t/ha respectively. 相似文献
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针对单一遥感数据已难以满足地质找矿工作需求的问题,本次研究综合使用雷达数据、光学数据及其他非遥感数据共同服务于地质找矿。以甘肃山羊坝地区为研究区,选择ASTER多光谱遥感数据,采用植被抑制法+特征向量主成分分析法,提取研究区的蚀变信息;选择PALSAR雷达数据,采用聚焦、多视、滤波、辐射定标、地理编码和增强处理等一系列处理方法制作雷达强度图,提取研究区构造信息。最后利用GIS平台进行遥感、地质及化探等信息的集成与综合分析,最终圈定了具有找矿前景的矿产资源靶区,野外查证发现一处金矿点。此次研究获得了良好的找矿效果,表明同时使用雷达数据、光学数据及其他非遥感数据的综合找矿方法,对本地区金矿找矿勘查具有重要的指导作用。 相似文献
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Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type. 相似文献
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以2015~2019年12景ALOS-2 PALSAR2影像和2018~2019年38景Sentinel-1A影像为主要数据源,利用PS-InSAR和SBAS-InSAR技术提取西藏江达县波罗乡白格滑坡点的形变信息,并对处理结果进行交叉验证。研究得到以下结论：1）PS-InSAR技术条件下,ALOS-2数据和Sentinel-1A数据的平均形变速率范围为-68.9~37.9 mm/a和-64.5~24.2 mm/a;SBAS-InSAR技术条件下,ALOS-2数据和Sentinel-1A数据的平均形变速率范围为-84.2~-40.0 mm/a和-84.0~-13.0 mm/a。2）对2种数据结果中提取的4个特征点进行时序分析和定量分析显示,2种InSAR技术结果变化趋势较为一致,验证了两者在滑坡监测中的可靠性和准确性。 相似文献
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针对PALSAR Level 1.1数据,研究使用NASA/JPL提供的开源干涉软件包ROI_PAC Version 3.0提取DEM.ROI_PAC的目前版本只能处理Level 1.0数据,因此,文章在分析了ROI_PAC软件包处理流程的基础上,提出处理Level 1.1数据的方法,并用PALSAR Level 1.1数据对该方法做了验证.干涉重建DEM与参考DEM的对比结果表明,二者的差异均值为0.27 m,标准差为±9.24 m,80%像元点的高程误差在±10 m以内. 相似文献
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Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm. 相似文献
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《Comptes Rendus Geoscience》2019,351(4):321-331
The aim of this paper is to map the aboveground biomass (AGB) in Gabon. First, a random forest (RF) model that relates reference AGB values to remote sensing (RS)-derived variables (mainly radar and optical images) was built, and the significant predictive variables were determined. Second, the built RF model was applied to the significant RS-derived variables to predict AGB across Gabon. The results showed that the overall RMSE (Root Mean Square Error) on the RS-derived AGB map with a spatial resolution of 50 m was 63.3 t/ha (R2 = 0.53).To improve the accuracy of the RS-derived AGB map, the integration of LiDAR data provided by the Geoscience Laser Altimeter System (GLAS) onboard the Ice Cloud and Land Elevation Satellite (ICESat) was investigated. First, an RF model that relates reference AGB values to GLAS-derived metrics and a DEM (Digital Elevation Model) was built. Second, the calibrated RF model was applied to obtain a spatially distributed estimation of AGB (GLAS footprints geolocation) covering forested areas in Gabon, with a density of 0.13 footprints/km2. Third, the semivariogram of residuals (RS-derived AGB map – GLAS-derived AGB “surrogate AGB”) was computed. Later, a regression kriging interpolation was performed by taking into account the spatial structure of residuals to provide a continuous residual map. Finally, the RS-derived AGB map and the residual map were summed, and a final AGB map was obtained. The results showed that the integration of GLAS surrogate AGB data slightly improves the accuracy of the RS-derived AGB map only for AGB values lower than 100 t/ha (bias and RMSE reduced by 13.9 and 10 t/ha, respectively). 相似文献