Description: 利用小波变换方法进行多卫星遥感图像数据融合，分析不同长度的小波基对融合图像的影响，从信息的保持性、视觉效果及运用灵活性等方面与IHS、PCA 融合算法进行了比较，从而探讨这一新算法在遥感图像分析应用中的可行性。-Using wavelet transform methods of multi-satellite remote sensing image data fusion, analysis of different wavelet bases on the length of the impact of image fusion, from the maintenance of information, visual effects and the use of flexibility with the IHS, PCA fusion algorithm are compared in order to explore the This new algorithm in the application of remote sensing image analysis feasibility. Platform: |
Size: 65536 |
Author:lieutenant |
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Description: 基于PCA的遥感图像融合，效果不错。可以作为遥感图像融合方面入门学习使用。-PCA-based remote sensing image fusion, good results. Integration of remote sensing images can be used as an introduction to use. Platform: |
Size: 159744 |
Author:葛宏 |
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Description: 1、试述PCA,MNF的基本流程,并比较其异同.
2、高光谱遥感的本质与特质是什么？请就你的认识说明原因。
文档内有整理的完整的信息。-1 Discussion on PCA, MNF' s basic processes, and compare their similarities and differences. 2, hyperspectral remote sensing of the nature and characteristics of that? Please explain your understanding of the reasons. Finishing within the document and complete information. Platform: |
Size: 19456 |
Author:张慧 |
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Description: 这是基于VC++编写的对遥感影像分析的程序，主要是对遥感影像进行PCA变换，使得对影响分析不仅可以依托于现成软件，我们自己也可以开发-This is written in VC++ based on remote sensing image analysis program, mainly for remote sensing images PCA transform, makes impact analysis can not only rely on ready-made software, we can also develop their own Platform: |
Size: 10659840 |
Author:毛晶 |
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Description: 本程序利用matlab语言，实现了对高光谱遥感图像的读取并做主成分分析，将结果按贡献率大小顺序排列并显示出来。-This procedure using matlab language to achieve a high spectral remote sensing image analysis components to read and call the shots, according to the results in order of contribution size and display. Platform: |
Size: 1024 |
Author:Aaron Yan |
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Description: 基于PCA的遥感影像融合Matlab程序，在Matlab 2011b上完美运行！-Remote sensing image fusion based on PCA Matlab, it works perfect on Matlab 2011b!
Platform: |
Size: 30720 |
Author:林忆 |
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Description: 应用matlab对多通道遥感影像进行主成分变换，就出含有最大信息量的主成分-Application of PCA matlab multi-channel remote sensing image, containing the principal component of the maximum amount of information Platform: |
Size: 1024 |
Author:吴琦 |
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Description: 基于PCA与HIS模型的高分辨率遥感影像阴影检测研究Shadow detection in high resolution remote sensing images of PCA and based on HIS model-Shadow detection in high resolution remote sensing images of PCA and based on HIS model Platform: |
Size: 2019328 |
Author:kk |
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Description: The fusion of high-spectral/low-spatial resolution
multispectral and low-spectral/high-spatial resolution panchromatic
satellite images is a very useful technique in various
applications of remote sensing. Recently, some studies showed
that a wavelet-based image fusion method provides high quality
spectral content in fused images. However, most wavelet-based
methods yield fused results with spatial resolution that is less
than that obtained via the Brovey, IHS, and PCA fusion methods.
In this paper, we introduce a new method based on a curvelet
transform, which represents edges better than wavelets. Since
edges play a fundamental role in image representation, one
effective means to enhance spatial resolution is to enhance the
edges. The curvelet-based image fusion method provides richer
information in the spatial and spectral domains simultaneously.
We performed Landsat ETM+ image fusion and found that the
proposed method provides optimum fusion results. Platform: |
Size: 1464320 |
Author:shaik |
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Description: The fusion of high-spectral/low-spatial resolution
multispectral and low-spectral/high-spatial resolution panchromatic
satellite images is a very useful technique in various
applications of remote sensing. Recently, some studies showed
that a wavelet-based image fusion method provides high quality
spectral content in fused images. However, most wavelet-based
methods yield fused results with spatial resolution that is less
than that obtained via the Brovey, IHS, and PCA fusion methods.
In this paper, we introduce a new method based on a curvelet
transform, which represents edges better than wavelets. Since
edges play a fundamental role in image representation, one
effective means to enhance spatial resolution is to enhance the
edges. The curvelet-based image fusion method provides richer
information in the spatial and spectral domains simultaneously.
We performed Landsat ETM+ image fusion and found that the
proposed method provides optimum fusion results. Platform: |
Size: 478208 |
Author:shaik |
Hits:
Description: The fusion of high-spectral/low-spatial resolution
multispectral and low-spectral/high-spatial resolution panchromatic
satellite images is a very useful technique in various
applications of remote sensing. Recently, some studies showed
that a wavelet-based image fusion method provides high quality
spectral content in fused images. However, most wavelet-based
methods yield fused results with spatial resolution that is less
than that obtained via the Brovey, IHS, and PCA fusion methods.
In this paper, we introduce a new method based on a curvelet
transform, which represents edges better than wavelets. Since
edges play a fundamental role in image representation, one
effective means to enhance spatial resolution is to enhance the
edges. The curvelet-based image fusion method provides richer
information in the spatial and spectral domains simultaneously.
We performed Landsat ETM+ image fusion and found that the
proposed method provides optimum fusion results. Platform: |
Size: 5374976 |
Author:shaik |
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Description: 高光谱图像PCA算法（注释详尽）
将传统的PCA算法应用于高光谱遥感中，实现光谱图像的数据降维-PCA algorithm for hyperspectral image
The traditional PCA algorithm is applied to hyperspectral remote sensing, the realization of spectral image data dimensionality reduction Platform: |
Size: 1024 |
Author:刘嘉诚 |
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Description: 基于pca权值的图像融合算法，在 遥感图像融合领域中作用显著(The image fusion algorithm based on PCA plays an important role in the field of remote sensing image fusion) Platform: |
Size: 2048 |
Author:明英
|
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Description: 高光谱遥感与传统的单波段、多光谱数据相比，波段量大量增加、波段宽度极大降低，对地面目标的光谱特性的测度更加细致，然而波段的增多必然导致数据量急剧增加、计算量增大、信息冗余增加以及统计参数的估计偏差增大。因此，对高光谱数据进行降维处理具有重要意义。一方面，降维能够使图像远离噪声，提高图像数据质量；另一方面，能够去除图像中的无价值波段，减少波段数，从而降低计算量，提高运算效率。主成分分析是常用的高光谱数据降维处理方法之一。(Compared with the single band, hyperspectral remote sensing and traditional multi spectral data, a substantial increase in the amount of band width of band, greatly reduced, measure spectral characteristics of ground objects in greater detail, but the increase will inevitably lead to a sharp increase in band data and increase the amount of calculation, and increase the information redundancy of statistical parameter estimation error. So it has important significance to reduce the dimensionality of hyperspectral data. On the one hand, the dimension reduction can make the image far away from noise and improve the quality of image data; on the other hand, it can remove the non value bands in the image, reduce the number of bands, thereby reducing the amount of calculation and improving the efficiency of the calculation. Principal component analysis is used for dimensionality reduction of hyperspectral data processing methods.) Platform: |
Size: 1895424 |
Author:太过安静
|
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Description: 基于主成分分析的遥感图像融合算法，融合结果质量较好。(The fusion algorithm of remote sensing image based on principal component analysis has better fusion result.) Platform: |
Size: 74752 |
Author:一枝梅231231 |
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