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41.
简要介绍了利用高分辨率卫星影像GeoEye-1的特点针对新疆边境不能实施航空摄影的特殊地带,提出一套利用最少控制点可满足基础测绘要求的技术实施方案. 相似文献
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基于两种遥感影像的郯庐断裂带构造解译浅析 总被引:1,自引:0,他引:1
吴哲 《测绘与空间地理信息》2011,34(1):181-182,185
利用光谱信息丰富的常规光学遥感(Landsat-7 ETM)和Google earth影像作为主要数据源,运用ETM1,4,7波段进行假彩色图像合成,综合两种不同的影像于ArcGIS平台上对郯庐断裂构造特征进行分析.首先确定线性构造,通过线性构造周围水系、地形特征推测断裂的存在,得到研究区域内郯庐断裂遥感影像图,基本确... 相似文献
43.
基于小波变换和HIS变换的海冰SAR与光学遥感影像融合方法研究 总被引:3,自引:2,他引:1
Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral information, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be improved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transformation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar(ASAR) loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensity-saturation(HIS) space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue(RGB) space, and the optical image from the China-Brazil Earth Resources Satellite(CBERS-02B) was transferred from the RGB space to the HIS space. The principal component analysis(PCA) method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification. 相似文献
44.
基于合成孔径雷达交叉极化通道数据的海上目标探测 总被引:1,自引:0,他引:1
Azimuth ambiguities(ghost targets) discrimination is of great interest with the development of a synthetic aperture radar(SAR). And the azimuth ambiguities are often mistaken as actual targets and cause false alarms. For actual targets, HV channel signals acquired by a fully polarimetric SAR are approximately equal to a VH channel in magnitude and phase, i.e., the reciprocity theorem applies, but shifted in phase about ± for the first-order azimuth ambiguities. Exploiting this physical behavior, the real part of the product of the two cross-polarized channels, i.e.(S S)HV VH, hereafter called A12 r, is employed as a new parameter for a target detection at sea. Compared with other parameters, the contrast of A12 r image between a target and the surrounding sea surface will be obviously increased when A12 r image is processed by mean filtering algorithm. Here, in order to detect target with constant false-alarm rates(CFARs), an analytical expression for the probability density function(pdf) of A12 r is derived based on the complex Wishart-distribution. Because a value of A12 r is greater/less than 0 for real target/its azimuth ambiguities, the first-order azimuth ambiguities can be completely removed by this A12r-based CFAR technology. Experiments accomplished over C-band RADARSAT-2 fully polarimetric imageries confirm the validity. 相似文献
45.
In this paper, a new snow wetness estimation model is proposed for full-polarimetric Synthetic Aperture Radar (SAR) data. Surface and volume are the dominant scattering components in wet-snow conditions. The generalized four component polarimetric decomposition with unitary transformation (G4U) based generalized surface and volume parameters are utilized to invert snow surface and volume dielectric constants using the Bragg coefficients and Fresnel transmission coefficients respectively. The snow surface and volume wetness are then estimated using an empirical relationship. The effective snow wetness is derived from the weighted averaged surface and volume snow wetness. The weights are derived from the normalized surface and volume scattering powers obtained from the generalized full-polarimetric SAR decomposition method. Six Radarsat-2 fine resolution full-polarimetric datasets acquired over Himachal Pradesh, India along with the near-real time in situ measurements were used to validate the proposed model. The snow wetness derived from the SAR data by the proposed model with in situ measurements indicated that the absolute error at 95% confidence interval is 1.3% by volume. 相似文献
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合成孔径雷达(SAR)海冰图像分割对全球气候研究和保证船舶航行安全具有重要意义。现有的基于区域的马尔可夫随机场(MRF)多极化SAR分割方法,由于受相干斑噪声影响,其区域划分不尽合理,不能有效完成分割。因此,提出一种噪声抑制的多极化SAR海冰图像分割算法,首先在极化总功率图上引入降低噪声的滤波算法,合理划分初始区域,其次考虑区域之间的差异度,从而实现多极化SAR海冰图像的准确分割。以RADARSAT-2和SIR-C获得的全极化海冰图像为实验数据进行验证,结果表明:和其他较先进算法相比,本文算法优势明显,既能高效保持图像连通性,又能增强图像的细节信息,具有更高的分割精度。 相似文献
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