各向同性同质区域选取的高光谱遥感图像噪声估计方法

在现有高光谱遥感图像噪声估计方法中，同质区域的选取通常是最关键的步骤，有效的同质区域选取方法能够提高图像的噪声估计精度。本文充分利用了高光谱遥感图像中丰富的空间信息和光谱信息，提出了一种各向同性同质区域选取算法，其中，为了更好地区分同质区域内像元相似度，构造了一种新的兰氏-光谱角度量；结合基于多元线性回归的去相关法，通过最优区域评估高光谱遥感图像噪声水平。利用不同结构及信噪比的模拟图像和真实高光谱遥感图像进行实验，通过与现有的多种噪声估计方法比较，验证了本文方法在针对不同噪声水平、不同复杂程度的图像时更加准确和稳定。

Noise Estimation Based on Isotropic Homogeneous Region Detection in Hypersepctral Images

It often plays a key role to extract homogeneous regions in the existing noise estimating methods for hyperspectral images (HSI). An effective homogeneous region detection method can improve the accuracy of image noise estimation. An isotropic homogeneous region detection algorithm (IHRDA) is proposed by using spatial information and spectral information, where a new Lance-SAD metric (LSM) is constructed to distinguish the similarity of picture elements in the homogeneous regions; then the noise level of hyperspectral images is estimated using the optimal regions with decorrelation based on multivariable linear regression (MLR) model. In experiments, synthetic images with different structure under different signal to noise ratio (SNR) and true hyperspectral remote sensing images are both compared with many existing methods, which show that the proposed method is more accurate and stable for hyperspectral images with various complexities and different noise levels.