基于边缘剔除的陆基高光谱图像噪声评估方法

为了准确地对陆基成像条件下高光谱图像的噪声水平进行评估, 提出了一种基于边缘剔除后残差调整的局部标准差法。首先将获取的高光谱图像分成若干个大小合适的子块, 而后利用Canny边缘检测算子检测出图像的边缘信息, 判断并剔除其中含有边缘的子块, 将剔除边缘子块后的均匀子块采用多元线性回归后求取残差的方法进行噪声估计。结果表明, 对同一幅陆基高光谱图像的不同子区域进行4×4像元与8×8像元分块, 得到的噪声总误差值分别为1.985×103与2.197×103。该噪声估计方法对陆基成像条件下高光谱图像的噪声评估具有较强的鲁棒性, 可为后续陆基高光谱图像处理与应用提供参考。

Noise evaluation method for land-based hyperspectral images based on edge elimination

In order to estimate the noise levels of hyperspectral images under ground-based imaging conditions accurately, a residual-scaled local standard deviations (RLSD) method after edge elimination was proposed. Firstly, the obtained hyperspectral image was divided into several sub-blocks of appropriate size, and then the edge information of the image was detected by using Canny edge detection operator, and the sub-blocks containing edges were judged and eliminated. The noise estimation of the uniform sub-blocks after the removal of edge sub-blocks was carried out by the method of multiple linear regression and residual error. The total error of noise was 1.985×103 and 2.197×103 for different sub-regions of the same land-based hyperspectral images by 4×4 pixel and 8×8 pixel segmentation. The results show that the proposed noise estimation method is robust to the noise evaluation of hyperspectral images under the condition of land-based imaging, which provides a reference for the subsequent processing and application of land-based hyperspectral images.