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IMAGE FUSION FOR DATA WITH DIFFERENT SPECTRAL RANGES

机译:具有不同光谱范围的数据的图像融合

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Images with low spatial resolution or limited spectral range can decrease the accuracy on remote sensing analysis, such as target detection for small substances and classification in homogenous area. However, collecting image data with high quality on spatial and spectral information is not always available for every studies and projects because of the economical and physical limitations. Image fusion methods combine the information of two or more data, and produce an image with sufficient information. Image fusion method was developed to enhance the series of bands of multispectral image using a panchromatic image on a satellite system first, but now it is applied to the multi-sensor images with different platforms and even the images with different wavelength ranges. In this study, image fusion was performed based on spectral unmixing model to enhance spectral and spatial information of images. Nonnegative matrix factorization technique also adopted to estimate optimized high frequency for image fusion. The unmixing-based image fusion method was applied to airborne data taken in different time, and data from unmanned aerial system with two different sensor were also used for the image fusion in this study. In this study, images with different spectral range taken from different sensor/time were spatially and spectrally enhanced, and the fusion results can improve the result of remote sensing analysis with its high spectral and spatial information.
机译:具有低空间分辨率的或限于光谱范围图像可以减少对遥感分析的准确度,例如在均质区域目标检测为小的物质和分类。然而,随着对空间和光谱信息的高品质采集图像数据并不总是可用的,因为经济和物理限制的每一个研究和项目。图像融合方法结合两个或两个以上的数据的信息,并产生具有足够信息的图像。图像融合方法的开发是为了增强该系列中的卫星系统上使用一个全色图像第一多光谱图像的频带,但现在它被施加到多传感器图像与不同的平台和甚至图像与不同波长范围。在这项研究中,根据光谱分离模型,以提高图像的光谱和空间信息,进行图像融合。非负矩阵分解技术还通过估计优化的高频的图像融合。基于解混的图像融合方法用两个不同的传感器也被用于本研究中的图像融合适用于在不同的时间取机载数据,并且数据从无人空中系统。在这项研究中,用不同的光谱范围内的图像从不同传感器拍摄/时间被空间和光谱增强,并且融合结果可改善与其高光谱和空间信息遥感分析的结果。

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