This model can separate spectral mixed pixels with very high accuracy.
该模型可以高度精确地分离出波谱混迭的象元。
Besides, many classification errors are caused by mixed pixels and speckle noise of the SAR image.
另外，许多分类错误是由SAR图像的像素点类别混淆和相干斑噪声干扰引起的。
A new method for the decomposition of mixed pixels of multi-channel remote sensing images is proposed.
提出一种新的对多通道遥感图像进行混合像元分解的方法。
Therefore, how to effectively interpret mixed pixels is an important problem of hyperspectral remote sensing applications.
因此，如何有效地解译混合像元是高光谱遥感应用的突出问题。
In order to improve the precision of remote sensing application, resolving the problem of unmixing the mixed pixels should be done.
为了提高遥感应用的精度，就必须解决混合像元的分解问题。
There are lots of mixed pixels in the low spatial resolution remote sensing images due to the limitation of the resolution of the satelite sensor.
由于传感器的分辨率的限制，在低空间分辨率遥感图像中存在着大量的混合像元。
Image segmentation brings errors in low or medium-resolution remote sensing images because a single pixel covers a larger area and there are a lot of mixed pixels.
对于中、低分辨率遥感图像来说，单个像元的面积较大，且混合像元现象严重，图像分割会产生较大的误差。
This article simulates misclassification because of mixed pixels on different spatial resolution images, then applies linear model to decompose mixed pixel, so as to improve classification accuracy.
该文模拟不同空间分辨率的遥感图像中混合像元造成的误分类情况，同时应用线性模型对混合像元进行了分解提纯，用以提高分辨率精度。
The chaotic mapping enables the logo pixels rearrange randomly in the specified spatial domain, and the mixed bits of the logo were embedded in the image DCT coefficients.
使用混沌映射将二进制商标的像素在空间区域中进行随机置乱，然后将置乱的信息分块嵌入在图像的DCT变换系数中。
The chaotic mapping enables the logo pixels rearrange randomly in the specified spatial domain, and the mixed bits of the logo were embedded in the image DCT coefficients.
使用混沌映射将二进制商标的像素在空间区域中进行随机置乱，然后将置乱的信息分块嵌入在图像的DCT变换系数中。
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