成像光谱技术为实现遥感定量分析、识别地物提供了一种重要的有效技术手段,但是成像光谱图像中往往含有严重程度不一的随机点噪声。要优化成像光谱图像质量,提高依据地物光谱特征探测、识别地表地物的有效性和精度,必须将图像中的随机点噪声予以消除或压抑。在分析已有各种随机点噪声消除或压抑方法的基础上,提出了一种基于小波分析理论的成像光谱图像随机点噪声消除方法。对成像光谱图像的试验处理及与邻域平均、中值滤波去噪声处理方法的对比分析结果表明,该方法不仅能高质量地消除图像中的随机点噪声,而且有效地保留了原图像中丰富的细微影纹和边缘信息。

Imaging Spectrometer is an important tool for quantitative analysis and discrimination of ground objects. The images of Imaging Spectrometer contain random noises to varying degrees of severity in all channels. To optimize the use of the images of an Imaging Spectrometer and to improve the effectiveness and accuracy of discriminating ground objects according to spectral absorption features, removal of random noises of images is necessary. Based on the analysis of previous methods of removing random noises of images, this paper develops a new method of removing random noises from Imaging Spectrometer images based on wavelet analysis (RRNW). Experimental results show that the performance of this method is better than neighbor average and median filter in removing random noises and reserving richer fine textures and edge information.