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1.
风速风向对SAR浅海水下地形成像影响的仿真研究 总被引：1，自引：0，他引：1
基于SAR浅海水下地形成像机理和M4S海面微波成像程序,建立了SAR浅海水下地形成像仿真模型,改进了传统浅海水下地形成像仿真模型的缺陷.通过仿真研究和分析浅海水下地形SAR图像特征实例,对风速风向与浅海水下地形SAR图像特征的关系提出了新的认识.低风速条件下,浅海水下地形SAR海面后向散射强度整体偏暗,高风速下整体偏亮;sAR图像条带亮暗的程度与风速有一定的关系,但不是主要的影响因素.风向对SAR浅海水下地形成像的影响明显,表现为,在逆风和顺风情况下,浅海水下地形SAR海面后向散射强度整体偏亮,SAR图像分别以亮条带和暗条带为主;侧风情况下,整体偏暗,SAR图像条带亮暗相当;最佳探测风向是逆风向. 相似文献
2.
Nakamura K. Wakabayashi H. Uto S. Ushio S. Nishio F. 《Geoscience and Remote Sensing Letters, IEEE》2009,6(2):277-281
To investigate the suitability of synthetic aperture radar (SAR) polarization data to estimate the sea-ice thickness in early summer in Lutzow-Holm Bay, Antarctica, we compared in situ ice thicknesses with the corresponding backscattering co-efficient for each polarization and the VV-to-HH backscattering ratio. The VV-to-HH backscattering ratio was derived from data acquired by ENVISAT Advanced SAR (ASAR). This ratio is related to the near-surface dielectric constant of the sea ice, which is, in turn, related to the developing process of ice and, thus, its thickness via changes in the near-surface sea-ice salinity. The sea ice encountered in the study area is close first-year pack ice and fast ice. For these old and relatively rough sea-ice types, the VV-to-HH backscattering ratio can be expected to depend on salinity-driven changes in the near-surface dielectric constant rather than changes of the surface roughness. We applied the empirical relationships between the ice thickness and the VV-to-HH backscattering ratio with the linear and logarithm fits to ASAR data. The linear fit gave the reliable result, with an rms error being 0.08 m and a correlation coefficient being 0.91, when compared to in situ fast-ice thickness. 相似文献
3.
星载SAR成像与SAR图像中一些不确定性因素分析 总被引：4，自引：0，他引：4
SAR成像中的不确定性因素不仅影响SAR成像质量,还给SAR图像的解译与应用带来困难。为了改善这些不确定性,从SAR成像的原理和过程出发,深入分析和探讨星载SAR成像与SAR图像中的多普勒参数估计、距离迁徙、斑点噪声和后向散射系数所产生的不确定性及带来的影响,并对部分不确定性提出相应的改善方法。利用实测ERS-2数据进行成像实验,实验结果显示SAR成像中的不确定性可以尽量得到改善,这对进一步研究SAR成像和应用有着实际意义。 相似文献
4.
Praveen K. Thakur Pradeep K. Garg Shiv P. Aggarwal Rahul D. Garg Sneh Mani 《Journal of the Indian Society of Remote Sensing》2013,41(4):933-945
The current study has used Synthetic Aperture Radar (SAR) satellite data to estimate the Snow Cover Area (SCA) in Manali watershed of Beas River in Northwest Himalayas of Himachal Pradesh, India. SAR data used in this study is of Radarsat-2 (RS2) and Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR). The SAR preprocessing was done with SAR image processing tools for converting raw SAR images into calibrated geo-coded backscatter images. Maps for forest, built area, layover and shadow were created and used for masking snow cover in these areas. The backscattering ratio of wet snow to reference image threshold method with value range from ?2 to ?3 db was used to estimate wet SCA for study area. In this technique, if the threshold is too high (≥-2 db) wet SCA is overestimated and if it is too low (≤-3db), this method underestimates the SCA. The wet SCA is under/over estimated (+6 % to?8 % on average) in late spring season due to the inherent terrain and SAR imaging effects of layover/foreshortening and shadow and also due to the masking of forest areas. Overall, the SCA derived from SAR data matches well when compared with total SCA derived from cloud free optical remote sensing data products, especially during wet season. 相似文献
5.
Single, interferometric dual, and quad-polarization mode data were evaluated for the characterization and classification of seven land use classes in an area with shifting cultivation practices located in the Eastern Amazon (Brazil). The Advanced Land-Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data were acquired during a six month interval. A clear-sky Landsat-5/TM image acquired at the same period was used as additional ground reference and as ancillary input data in the classification scheme. We evaluated backscattering intensity, polarimetric features, interferometric coherence and texture parameters for classification purposes using support vector machines (SVM) and feature selection. Results showed that the forest classes were characterized by low temporal backscattering intensity variability, low coherence and high entropy. Quad polarization mode performed better than dual and single polarizations but overall accuracies remain low and were affected by precipitation events on the date and prior SAR date acquisition. Misclassifications were reduced by integrating Landsat data and an overall accuracy of 85% was attained. The integration of Landsat to both quad and dual polarization modes showed similarity at the 5% significance level. SVM was not affected by SAR dimensionality and feature selection technique reveals that co-polarized channels as well as SAR derived parameters such as Alpha-Entropy decomposition were important ranked features after Landsat’ near-infrared and green bands. We show that in absence of Landsat data, polarimetric features extracted from quad-polarization L-band increase classification accuracies when compared to single and dual polarization alone. We argue that the joint analysis of SAR and their derived parameters with optical data performs even better and thus encourage the further development of joint techniques under the Reducing Emissions from Deforestation and Degradation (REDD) mechanism. 相似文献
6.
Hyangsun Han Jae-In Kim Chang-Uk Hyun Seung Hee Kim Jeong-Won Park Young-Joo Kwon 《地理信息系统科学与遥感》2020,57(5):650-669
ABSTRACT Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer. 相似文献
7.
André Medeiros de Andrade Jorge Arigony-Neto Ulisses Franz Bremer Roberto Ferreira Machado Michel Alice César Fassoni-Andrade Carlos Ernesto Goncalves Reynaud Schaefer 《国际地球制图》2016,31(7):803-812
The climate change phenomena have been influencing terrestrial and glacial ecosystems around the planet. Maritime Antarctica is especially sensitive to these climate variations and over the last 50 years increasing global air temperatures have caused extensive glacial retreat. The objective of this study is to evaluate the potential use of the SAR technology in monitoring the surface dynamics of the Potter Peninsula, King George Island, maritime Antarctica. An image generated by the SAR satellite COSMO-SkyMed, obtained on 2 February 2011, was used to extract the backscattering values of targets on the surface for further processing and classification, using a supervised statistic classifier of maximum likelihood for the determination of the surface classes. The average backscattering of water bodies presented high similarity, which made its separation unattainable. On the other hand, the surface classes’ bare ice and wet snow over the glacier presented distinct average backscattering values, which allowed an efficient and precise classification using only this parameter. The classification process showed satisfactory results for periglacial environments, presenting high fidelity to the field data. 相似文献
8.
基于图像特征的星载SAR图像模拟研究 总被引：3，自引：0，他引：3
SAR图像模拟技术被广泛应用于SAR系统的设计和验证、SAR图像的正射纠正、雷达图像解译和目标识别等。随着星载SAR的发展,必然面临着对星载SAR图像模拟的大量需求。本文首先从SAR图像的几何特征和辐射特征出发,探讨了SAR图像模拟技术的原理,分析了RD（Rang Doppler）模型,后向散射模型和斑噪模型。在传统RD模型的基础上,根据不同地形特征（起伏地形和平坦地形）考虑不同的后向散射模型。特别强调了在平坦地形情况下,需要地物分类数据的参与,并利用Ulaby和Dobson的后向散射模型。另外,在SAR图像统计特征的基础上,进行SAR图像的乘性噪声模拟,可以满足更逼真的SAR场景需求。然后,给出了图像模拟的算法流程,并对关键步骤的算法做了分析。最后,在实现基于图像特征的星载SAR图像场景模拟算法的基础上,选择新疆窝依牙地区和天津地区分别进行起伏地形和平坦地形的模拟试验,实验结果证明了本文模拟算法的有效性。 相似文献
9.
10.
Varghese Ouseph Alappat Ashok Kumar Joshi Y. V. N. Krishnamurthy 《Journal of the Indian Society of Remote Sensing》2011,39(4):583-589
Optical remote sensing data have been extensively used to derive biophysical properties that relate forest type and composition.
However, stand density, stand height and stand volume cannot be estimated directly from optical remote sensing data owing
to poor sensitivity between these parameters and spectral reflectance. The ability of microwave energy to penetrate within
forest vegetation makes it possible to extract information on both the crown and trunk components from radar data. The type
of polarization employed determines the radar response to the various shapes and orientations of the scattering mechanisms
within the canopy or trunk. This study mainly presents experimental results obtained with airborne E-SAR using polarimetric
C and L bands over the tropical dry deciduous forest of Chandrapur Forest Division, Maharashtra. A detailed documentation
of the relationship between SAR C & L bands backscattering and forest stand variables has been provided in the present study
through linear correlation. Linear correlation of the single channel SAR derived estimates with the field measured means show
a good correlation between L HV backscattering coefficient with stand volume (r2 = 0.71) and L HH backscattering coefficient with stand density (r2 = 0.75). The results imply that SAR data has significant potential for stand menstruation in operational forestry. 相似文献
11.
XianWen Ding XiaoFeng Li 《International Journal of Applied Earth Observation and Geoinformation》2011
As an active microwave remote sensing sensor, synthetic aperture radar (SAR) can image the Earth surface with high spatial resolution in both day and night under all weather conditions. In this paper, a digital image processing technique was implemented to extract water area information from SAR images and the result is used to monitor the water area variation of Lake Dongting, the second largest freshwater lake in China. 8-year time series of European Space Agency's ENVISAT ASAR (Advanced Synthetic Aperture Radar) images acquired between 2002 and 2009 were obtained and a land-water classification scheme was implemented. Using independent in situ water level data measured at a lake-side hydrologic station during study period, we derived the relationship between water level and water area of Lake Dongting. The results show that, (1) during dry seasons, the water area is 518 km2 larger than that in the 1990s reported by Yangtze BHYRWRC (Bureau of Hydrology and Yangtze River Water Resources Commission), 2000; (2) the water area of Lake Dongting increased significantly in the 2000s after the Chinese Government's “return land to lake” policy took effect in 1998; (3) the water level of Lake Dongting could be low during a rainy season due to drought; but could be high in a dry season due to discharges from the upstream Three Gorges Dam. In addition, the relationship between water storage change and water area/level change is obtained. 相似文献
12.
极化干涉SAR数据地表土地类型分类 总被引：2，自引：0，他引：2
基于新疆和田地区1994年10月9日和10日SIR-C-L波段全极化雷达数据。首先对极化干涉测量的基本原理和数据处理流程进行了详细的阐述，接着，用Cloude相干最优算法得到了与3种地物散射机制相对应的3个最优相干图。并且就地物相干性对极化的强烈依赖和3种散射机制中地物的最优相干特性进行了分析，具有最高相干值的相位图在提取DEM方面较有利，具有最低相干值的相干图在地物识别方面较有利。最后，在对最优相干系数。后向散射系数和熵进行数据相关性分析基础上，利用得到的最优相干系数，熵和后向散射系数数据进行了土地类型的识别和分类，得到了很好的效果。 相似文献
13.
高分三号影像水体信息提取 总被引：4，自引：0，他引：4
国内外针对陆地水体信息提取、洪涝灾害快速响应方面具有较深入的研究，但是多采用发展较早、图像质量可靠的可见光影像及国外星载SAR影像。中国合成孔径雷达（SAR）卫星高分三号（GF-3）已获取了大量多极化、全极化SAR数据，为了将GF-3影像快速应用到环境保护、水资源管理等行业中，本研究分析了水体与其他目标具有的不同后向散射特性，将阈值分割法与马尔可夫随机场（MRF）相结合，发展了一种检测精度较高、自动化程度强的水体信息提取方法。该方法首先通过直方图统计的方法对不同成像模式、不同极化的GF-3影像进行后向散射强度分析，在阈值分割的研究基础上，比较了最大类间方差法（Otsu）和Kittler and Illingworth（KI）二值化法在水体-非水体分类中的效果。然后结合DEM和GF-3轨道参数排除因阴影现象产生的辐射失真对图像概率分布的影响，得到初始的水体信息分布图，再经过Fisher变换和马尔可夫随机场（MRF）的迭代运算，综合利用GF-3影像的多极化信息和空间上下文信息，以最大后验概率准则输出最终的水体分布图。利用了湖南省东北部不同成像模式的两景GF-3影像进行试验，在成像时间接近的光学影像中随机选择检验样点进行精度评价。实验结果表明，KI方法在GF-3水体提取应用中比Otsu方法具有更强的优势，剔除图像阴影区域后，自动化确定的阈值与目视解译阈值更加接近，通过MRF模型优化以后，实现了对水体信息的连贯提取，对图像噪声具有较强的抑制作用。本研究对水体目标的提取精度均达到了85%以上，实验结果精度优于基于光学影像的水体指数法，整个流程需要很少的人工经验参与，具有自动化程度强、检测精度高的优势。 相似文献
14.
Influence of surface and vegetation characteristics on C-band radar measurements for soil moisture content 总被引：1，自引：0，他引：1
Shakil Ahmad Romshoo Masahiro Koike Sadayukihir Onaka Taikan Oki Katumi Musiake 《Journal of the Indian Society of Remote Sensing》2002,30(4):229-244
Soil moisture estimation using microwave remote sensing faces challenges of the segregation of influences mainly from roughness and vegetation. Under static surface conditions, it was found that Radarsat C-band SAR shows reasonably good correlation and sensitivity with changing soil moisture. Dynamic surface and vegetation conditions are supposed to result in a substantial reduction in radar sensitivity to soil moisture. A C-band scatterometer system (5.2 GHz) with a multi-polarization and multi-angular configuration was used 12 times to sense the soil moisture over a tall vegetated grass field. A score of vegetation and soil parameters were recorded on every occasion of the experiment. Three radar backscattering models Viz., Integral Equation Model (IEM), an empirical model and a volume scattering model, have been used to predict the backscattering phenomena. The volume scattering model, using the Distorted Born Approximation, is found to predict the backscattering phenomena reasonably well. But the surface scattering models are expectedly found to be inadequate for the purpose. The temporal variation of soil moisture does show good empirical relationship with the observed radar backscattering. But as the vegetation biomass increases, the radar shows higher sensitivity to the vegetation parameters compared to surface characteristics. A sensitivity analysis of the volume scattering model for all the parameters also reveals that the radar is more sensitive to plant parameters under high biomass conditions, particularly vegetation water content, but the sensitivity to surface characteristics, particularly to soil moisture, is also appreciable. 相似文献
15.
Paloscia S. Macelloni G. Pampaloni P. Santi E. 《Geoscience and Remote Sensing Letters, IEEE》2004,1(3):201-205
The sensitivity of radar backscattering to the principal hydrological parameters, such as vegetation biomass, soil moisture, and surface roughness, is discussed. Results obtained by using multifrequency synthetic aperture radar (SAR) data measured by the Jet Propulsion Laboratory Airborne Synthetic Aperture Radar, Spaceborne Imaging Radar-C, and European Remote Sensing 1/2 sensors are summarized. The sensitivity of L- and C-bands to spatial variations of plant and soil parameters is masked by the presence of surface roughness, which in turn affects the radar signal. However, from the observation of data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture and vegetation biomass is found to be significant, since the effects of spatial variations are smoothed. On the other hand, the sensitivity to surface roughness becomes appreciable when multitemporal data are averaged in time, thus reducing the effects of temporal moisture variations. 相似文献
16.
This research letter presents preliminary results of mapping rice crop growth using ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization HH/HV data. Four ASAR HH/HV images were collected in the early rice-growth cycle in the test site in 2006, and the temporal response of ASAR data to the rice field was analyzed. The height and biomass of rice were measured during acquisition of ASAR data, and empirical relationships were established between the backscattering coefficient and these two parameters. Based on the temporal variation of the radar response, a method for mapping a rice growth area was developed using the combination of ASAR HH and HV polarization data between two acquisition dates. The results confirm that C-band SAR data have great potential in the development of an operational system for monitoring rice crop growth in Southern China. 相似文献
17.
针对帕米尔高原堰塞湖——萨雷兹湖的安全问题及水文站点资料获取困难,缺乏时段固定、精度统一的准实时湖泊水位信息等问题,本文提出了基于星载激光雷达测高数据（ICESat-1/2）的高原湖泊水位综合反演。开展了近20年（2003—2019年）的水位变化遥感调查和时序重建研究,并采用趋势分析和Mann Kendall （M-K）非参数检验,对近20年萨雷兹湖的水位变化特征进行过程分析,初步揭示了萨雷兹湖水位的近期变化趋势和规律。结果表明：①ICESat-1/2激光测高雷达数据在陆地湖泊水位反演中误差可控制在0.05 m之内,精度高度可信,为无/缺资料地区湖泊水文监测提供良好的数据源;②2003—2019年,萨雷兹湖的水位持续呈现显著上升的趋势,水位上升的速率约为0.15 m/a （p<0.01,双尾）;③萨雷兹湖的水位年内波动较大（至少7~8 m）。最高水位出现在9—10月,当前平均水位约为3265 m,最低水位出现在3—5月,当前平均水位约为3259 m。以上研究结果可为萨雷兹湖的综合治理及安全评估提供数据支持和技术参考。 相似文献
18.
Designing and validating digital soil mapping (DSM) techniques can facilitate precision agriculture implementation. This study generates and validates a technique for the spatial prediction of soil properties based on C-band radar data. To this end, (i) we focused on working at farm-field scale and conditions, a fact scarcely reported; (ii) we validated the usefulness of Random Forest regression (RF) to predict soil properties based on C-band radar data; (iii) we validated the prediction accuracy of C-band radar data according to the coverage condition (for example: crop or fallow); and (iv) we aimed to find spatial relationship between soil apparent electrical conductivity and C-band radar. The experiment was conducted on two agricultural fields in the southern Argentine Pampas. Fifty one Sentinel 1 Level-1 GRD (Grid) products of C-band frequency (5.36 GHz) were processed. VH and VV polarizations and the dual polarization SAR vegetation index (DPSVI) were estimated. Soil information was obtained through regular-grid sample scheme and apparent soil electrical conductivity (ECa) measurements. Soil properties predicted were: texture, effective soil depth, ECa at 0-0.3m depth and ECa at 0-0.9m depth. The effect of water, vegetation and soil on the depolarization from SAR backscattering was analyzed. Complementary, spatial predictions of all soil properties from ordinary cokriging and Conditioned Latin hypercube sampling (cLHS) were evaluated using six different soil sample sizes: 20, 40, 60, 80, 100 and the total of the grid sampling scheme. The results demonstrate that the prediction accuracy of C-band SAR data for most of the soil properties evaluated varies considerably and is closely dependent on the coverage type and weather dynamics. The polarizations with high prediction accuracy of all soil properties showed low values of σVVo and σVHo, while those with low prediction accuracy showed high values of σVVo and low values of σVHo. The spatial patterns among maps of all soil properties using all samples and all sample sizes were similar. In conditions when summer crops demand large amount of water and there is soil water deficit backscattering showed higher prediction accuracy for most soil properties. During the fallow season, the prediction accuracy decreased and the spatial prediction accuracy was closely dependent on the number of validation samples. The findings of this study corroborates that DSM techniques at field scale can be achieved by using C-band SAR data. Extrapolation y applicability of this study to other areas remain to be tested. 相似文献
19.
Mehdi Hosseini Mohammad Reza Saradjian 《Journal of the Indian Society of Remote Sensing》2014,42(4):719-726
High difference between dielectric constant of water (dielectric constant about 80) and dielectric constant of dried soil (dielectric constant about 2–3) makes Synthetic Aperture Radar (SAR) highly capable in soil moisture estimation. However, there are other factors which affect on radar backscattering coefficient. The most important parameters are vegetation cover, surface roughness and sensor parameters (frequency, polarization and incidence angle). In this paper, the importance of considering the effects of these parameters on SAR backscatter coefficients is shown by comparing different soil moisture estimation models. Moreover, an experimental soil moisture estimation model is developed. It is shown that this model can be used to estimate soil moisture under a variety of vegetation cover densities. The new developed model is based on combination of different indices derived from Landsat5-Thematic Mapper and AIRSAR images. The AIRSAR image is used for extraction of backscattering coefficient and incidence angle while TM image is used for calculation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Brightness Temperature. Then a soil moisture estimation model which is named as Hybrid model is developed based on integration of all of these parameters. The accuracies of this model are assessed in the NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7 by using SAR data in C band and L band frequencies and also in different polarizations of HH, HV, VV and TP. The results show that for instance in L band with HV polarization, R-square values of 0.728, 0.628 and 0.527 are obtained between ground measured soil moisture and estimated soil moisture values using the Hybrid model for NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7, respectively. 相似文献
20.
山区土壤含水量对山区植被生长监测、滑坡预测等工作具有重要意义,因此针对山地低矮植被区域,提出了全极化SAR图像的土壤含水量估计方法。为解决山地区域SAR图像几何形变和极化旋转问题,根据入射角、坡度、坡向信息定义了可测区域与不可测区域,并对可测区域后向散射系数进行校正。其次以密西根模型为基础,发展了低矮植被的散射模型。在假定植被和土壤特征不变的情况下,基于此散射模型并结合校正数据建立了山区土壤含水量反演方法。结果表明,模型反演的土壤含水量和实验点实测值基本一致,两个实验点反演值分别为14%和15%,实测值为11.45%和15.80%,能够满足一般应用的需求。 相似文献