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The Generating Mechanism of strips and Destriping Algorithm of HJ-1A Hyperspectral Image

机译:HJ-1A高光谱图像条的产生机理及去斑算法

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Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns appear in the data. The strips usually found in images acquired by push-broom sensors, which are characterized by a high degree of spatial and spectral coherence. Many strips-reduction approaches such as histogram matching and moment matching have been developed. These methods assume that all sensor elements observe similar subscenes in a given image and adjust the distributions of values acquired by each sensor to some reference distribution by means of a histogram or moment matching, but this assumption usually is failure in many scenes which contain diverse materials. The formation of strips has close connection with the image formation process of push-broom imaging spectrometers. Many causes such as the uniformity of the pixels, the push-broom mode and the asymmetric width of thin slit at the entrance of imaging spectrometers can induce the strips in the images. Comparing with the dispersive spectrometers, interferometer spectrometers acquire the interference data, obtaining the spectrum by using the Fast Fourier Transformation (FFT). By analyzing the generating mechanism of strips in push-broom interferometer imaging spectrometers, we proposed an approach that corrects the strips using relative calibration factor directly computed from the acquired image. Once the relative calibration factor is determined, all the images acquired by the same imaging spectrometers can be corrected. So the methodology is an efficient one to reduce the strips. A formula is set up to describe the connection between gray values of pixels in images and relative calibration factor. The developed methodology is tested on data acquired by HJ-1A Hyperspectral Imaging Spectrometers, which is an interferometer spectrometer put into operation in 2008. The shortwave bands of HJ-1A HSI have severe strips. Results show excellent rejection of the noise with respect to the original HJ-1A HSI images, improving the removal in those scenes with diverse materials as well as being high efficient.
机译:高光谱遥感影像会受到不同类型噪声的影响。除了典型的随机噪声外,数据中还会出现非周期性的部分确定性干扰模式。通常在推扫式扫帚传感器获取的图像中发现这些条带,这些条带的特征是高度的空间和光谱相干性。已经开发了许多减少条带的方法,例如直方图匹配和矩匹配。这些方法假定所有传感器元素在给定图像中观察到相似的子场景,并通过直方图或矩匹配将每个传感器获取的值的分布调整为某个参考分布,但是这种假设通常在包含不同材质的许多场景中都是失败的。条的形成与推扫式成像光谱仪的图像形成过程密切相关。诸如像素的均匀性,推扫模式和成像光谱仪入口处狭缝的不对称宽度等许多原因会导致图像中出现条纹。与色散光谱仪相比,干涉仪光谱仪获取干涉数据,并通过快速傅立叶变换(FFT)获得光谱。通过分析推扫式干涉仪成像光谱仪中条带的产生机理,我们提出了一种使用直接从采集的图像中计算出的相对校准因子校正条带的方法。一旦确定了相对校准因子,就可以校正由相同成像光谱仪采集的所有图像。因此,该方法是减少条带的一种有效方法。建立一个公式来描述图像中像素的灰度值与相对校准因子之间的联系。所开发的方法论已在HJ-1A高光谱成像光谱仪(该干涉仪光谱仪于2008年投入运行)获取的数据上进行了测试。HJ-1AHSI的短波带有很强的条纹。结果表明,相对于原始的HJ-1A HSI图像,噪声具有出色的抑制能力,从而提高了使用各种材质在这些场景中的去除效果,并提高了效率。

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