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Optimizing Classification Accuracy of Remotely Sensed Imagery with DT-CWT Fused Images

机译:利用DT-CWT融合图像优化遥感影像的分类精度

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Image fusion is a basic tool for combining low spatial resolution multi-spectral and high spatial resolution panchromatic images using advanced image processing techniques. Study on efficient image fusion method for specific application is one of the most important objectives in current remote sensing community. On the other hand, it is well known that the image classification techniques combine complex processes that may be affected by factors like the resolution of remote sensed images. This study focuses on the influence of image fusion on spectral classification algorithms and their accuracy. Results are presented on SPOT images. The best results were achieved by Dual Tree Complex Wavelet Transform (DT-CWT)).
机译:图像融合是使用高级图像处理技术将低空间分辨率多光谱图像和高空间分辨率全色图像组合在一起的基本工具。针对特定应用的有效图像融合方法的研究是当前遥感界最重要的目标之一。另一方面,众所周知,图像分类技术结合了可能受诸如遥感图像的分辨率之类的因素影响的复杂过程。这项研究的重点是图像融合对光谱分类算法及其准确性的影响。结果显示在SPOT图像上。通过双树复数小波变换(DT-CWT)获得了最佳结果。

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