摘要
目的高光谱图像距具有较高的光谱分辨率,从而具备区分诊断性光谱特征地物的能力,但高光谱数据经常会受到如环境、设备等各种因素的干扰,导致数据污染,严重影响高光谱数据在应用中的精度和可信度。方法根据高光谱图像光谱维度特征值大小与所包含信息的关系,利用截断核范数最小化方法表示光谱低秩先验,从而有效抑制稀疏噪声;再利用高光谱图像的空间稀疏先验建立正则化模型,达到去除高密度噪声的目的;最终,结合上述两种模型的优势,构建截断核范数全变差正则化模型去除高斯噪声、稀疏噪声及其他混合噪声等。结果将本文与其他三种近期发表的主流去噪方法进行对比,模型平均峰信噪比提高3.20 dB,平均结构相似数值指标提高0.22,并可以应用到包含各种噪声、不同尺寸的图像,其模型平均峰信噪比提高1.33 dBo结论本文方法在光谱低秩中更加准确地表示了观测数据的先验特征,利用高光谱遥感数据的空间和低秩先验信息,能够对含有高密度噪声以及稀疏异常值的图像进行复原。
Objective Hyperspectral remote sensing is a technique based on the principle of spectrometry to obtain some very narrow and continuous image data in the ultraviolet, visible, near-infrared and mid-infrared regions of the electromagnetic spectrum. Hyperspectral imaging technology combines the traditional two-dimensional image remote sensing technology and spectral technology to obtain the surface image and the spectral information at the same time. Hyperspectral images(HSI) can not only classify and recognize ground objects with high spectral diagnostic ability, but also contain rich information, which makes them widely used in many fields. The unique characteristics of hyperspectral images bring convenience and advantages to the acquisition of geographic information and the identification of ground objects. Unfortunately, there are also some difficulties in hyperspectral technology: the amount of data obtained by hyperspectral sensors is large, but it is often interfered by various factors during the acquisition process,such as environment and equipment, so that the data is polluted, which reduces the data availability and limits the subsequent application of hyperspectral sensors in various fields. Therefore, reducing noise pollution of data, obtaining more effective image information and increasing the utilization rate of image data are important links to ensure that hyperspectral images can play an important role in subsequent applications. Method Hundreds of continuous spectral bands image the target region at the same time, so that the hyperspectral image can provide spatial and spectral domain information. Moreover, the continuity of hyperspectral images in spatial domain and spectral domain makes the correlation between adjacent channels strong, that is a low-rank property. Based on this feature, spectral low-rank priors or spatial low-rank priors are considered to establish the restoration model for hyperspectral data restoration. Undoubtedly, the combination of the two models can achieve better recovery
作者
杨润宇
贾亦雄
徐鹏
谢晓振
Yang Runyu;Jia Yixiong;Xu Peng;Xie Xiaozhen(College of Science,Northwest A&F University,Yangling 712100 y China)
出处
《中国图象图形学报》
CSCD
北大核心
2019年第10期1801-1812,共12页
Journal of Image and Graphics
基金
国家自然科学基金项目(61401368)
西北农林科技大学创新训练项目(201710712082)
关键词
高光谱遥感图像
图像复原
低秩先验
截断核范数
全变差
正则化方法
hyperspectral remote sensing image
image restoration
low-rankprior
truncated nuclear norm regularization
total variation(TV)
regularization method
