残差均方 meaning in Chinese

residual mean square

Each band of hyperspectral image has the same physical structure , so we classification the first band , and design an optimal linear predictor for each class to make the mean prediction square error minimal , and then we use jpeg - ls algorithm to remove the spatial redundancy
由于高光谱图像每个波段都具有相同的物理结构,先对首幅图像进行分类,在每个子类中分别使用各自的最佳线性预测器,将该类中的相邻谱段进行预测并将预测残差均方降为最小,然后用jpeg - ls算法去除残差图像的相关性。
Secondly , hyperspectral images are hard to compress because of their abundant details , complicated texture and insignificant special correlation . making use of the significant spectral correlation within the hyperspectral images , we propose an optimal linear predictor which makes the square error minimal
针对高光谱遥感图像细节丰富纹理复杂,空间相关性弱,难于压缩的特点,本文充分利用了高光谱遥感图像的谱间相关性,设计出对相邻谱段进行预测并将预测残差均方降为最小的一种最佳线性预测器。