针对目前应用Savitzky-Golay(SG)滤波器进行高光谱图像滤波过程中空间信息无法利用, 造成小麦赤霉病高光谱分类识别模型精度仅能达到87.088 9%的问题, 提出高光谱图像空-谱维联合SG滤波(TSG滤波)的方法, 使模型精度相比采用SG滤波提升了12.066 7%。 该算法将一维的SG卷积核按四个方向扩展成二维的SG卷积核, 根据卷积定理利用快速傅里叶变换对高光谱图像数据进行空间及光谱维联合快速滤波。 设置TSG滤波核窗口系数m=2~4, 阶数n=3~5, 滤波后图像信噪比提升了10%以上、 峰值信噪比高于30 db、 结构相似度大于96%, TSG滤波能保持原图特征并显著提升图像信噪比。 对比Pavia University高光谱图像经TSG滤波(m=3, n=4)、 SG滤波(m=7, n=3)后的灰度图像与光谱图, 可以看出TSG滤波后图像条带噪声得到了抑制、 特征峰相对峰值高度最高提升31.68%、 特征波段平均强度提升41.83%, 而SG滤波后图像条带噪声依然清晰且特征峰相对峰值高度至少降低了13.40%。 构建基于TSG-PCA-SVM算法的小麦赤霉病高光谱分类识别模型, 训练集包含500个样本点, 测试集包含4500个样本点, 模型测试集分类精度高达99.155 6%、 卡帕系数0.983 613, 对于小麦高光谱数据集中全部116 880个样本点的总分类精度高达99.206 0%。 经TSG滤波后分类模型精度高、 一致性好, 分类精度相较于SG滤波后的87.088 9%获得了显著提升。 综上所述, 该研究为高光谱图像滤波提供了一种新的思路, 为小麦赤霉病高光谱识别系统构建提供了参考与依据。

In the process of hyperspectral image(HI) filtering by Savitzky-Golay(SG) filter, the spatial information of HI is ignored which lead to the accuracy of recognition model of wheat HI dataset can only reach 87.088 9%. This paper proposes a method, TSG filter, that combines space-spectrum information of HI. By this way, the accuracy of the model is improved by 12.066 7% compared with SG filter. The algorithm expands one-dimensional SG convolution core into two-dimensional SG convolution core in four directions. Then the HI data can be quickly convoluted using convolution theorem and fast Fourier transform, so that the space-spectrum noise of HI can quickly filter. When the TSG filter core coefficient m=2~4, n=3~5, the SNR of the HI is increased by more than 10%, the PSNR is higher than 30 dB, and the SSIM is greater than 96%, which means TSG filter maintains the original HI characteristics well and improve the SNR significantly. After TSG filtering (m=3, n=4) or SG filtering (m=7, n=3), by comparing with the gray image and spectrogram of Pavia University HI, it can be seen that after TSG filtering, the band noise of the image is suppressed, the peak height of the characteristic peaks is increased by 31.68%, and the average intensity of the characteristic band is increased by 41.83%, while after SG filtering the band noise of the image is still clear and the relative peak height of the characteristic peak is up to 13.40%. The model of wheat HI recognition model based on TSG-PCA-SVM algorithm is constructed. The training set contains 500 sample points and the test set contains 4 500 sample points. The accuracy of this model is as high as 99.155 6% and the kappa coefficient is 0.983 613 while predicting the test set. The total accuracy predicting all 116 880 samples in the wheat HI data set is as high as 99.206 0%, which means the classification model has high accuracy and good consistency, and the classification accuracy is significantly improved compared with SG filtering that only reaches 87.088 9%. In conclusion, this paper provides a new idea for HI filtering and provides a reference for the construction of a hyperspectral identification system of wheat Hi dataset.