【摘要】 高光谱图像取得较高的光谱分辨率对于分类和识别很有益。但与此同时也带来了巨大的数据量,使其压缩成为必需。传统的预测方法能够在一定程度上去除谱带之间的相关性,但其预测系数不能利用高光谱图像谱带间的信息进行自适应的调整,使得预测效果不是最优。本文建立了高光谱图像谱带间的线性模型,推导出在信噪比最优下的预测。该方法能够更好地降低预测后图像的熵值。实验表明,相对于传统方法重建的平均信噪比提高了4.6064 dB。更多还原

【Abstract】 Hyperspectral Image(HSI) can obtain a high spectral resolution and it is important for classification and detection.Meanwhile the enormous data volume is brought,so HSI is necessary to be compressed.Traditional prediction method can decorrelate the band correlation of HSI,but the result is not optimal.The linear model for HSI is established,and the best prediction is deduced under the sense of SNR.The method can obtain a lower entropy after prediction.Simulation results show that compared with the traditional algorithm,the method increases 4.606 4 dB in SNR in average.更多还原