基于超像素块聚类与低秩特性的高光谱图像降噪  

Hyperspectral Image Denoising Based on Superpixel Block Clustering and Low-Rank Characteristics

作　　者：张明华[1] 武玄 宋巍 梅海彬[1] 贺琪[1] 苏诚 ZHANG Minghua;WU Xuan;SONG Wei;MEI Haibin;HE Qi;SU Cheng(College of Information Technology,Shanghai Ocean University,Shanghai 201306,China;East China Sea Forecast Center,Ministry of Natural Resources,Shanghai 200136,China)

机构地区：[1]上海海洋大学信息学院,上海201306 [2]自然资源部东海预报中心,上海200136

出　　处：《数据采集与处理》2023年第3期549-564,共16页Journal of Data Acquisition and Processing

基　　金：国家重点研发计划(2021YFC3101601);国家自然科学基金面上项目(61972240,41906179);上海市科委地方能力建设项目(20050501900)。

摘　　要：高光谱图像通常受到高斯噪声、脉冲噪声、死线和条纹等干扰,因此去噪必不可少。现有基于低秩特性的降噪方法通过引入空间信息改善了降噪效果,但由于其只利用了局部相似性或非局部自相似性,而对在光谱维度存在一定结构信息的稀疏噪声去除效果较差。本文提出了基于超像素块聚类与低秩特性的高光谱图像降噪方法,实现了分块的自适应划分与聚类,在较好地保留了局部细节的同时又充分利用了非局部空间自相似性,且实验表明聚类后的超像素块组成的同物分块具有良好的空-谱双重低秩属性。该方法首先对高光谱图像进行超像素分割,再对超像素块进行聚类,得到同物分块;然后对其建立低秩矩阵恢复模型并求解,最终得到降噪后图像。本文分别在模拟数据和真实数据上进行实验,并与其他基于低秩特性的方法进行比较,结果表明:本文方法对混合噪声,尤其是具有一定结构信息的稀疏噪声具有较好的降噪性能。Hyperspectral images are usually contaminated by Gaussian noise,impulse noise,dead lines and stripes.So,denoising is an essential step.The existing denoising methods based on low-rank characteristics introduce spatial information to improve the noise reduction effect.But because they often only use local similarity or non-local self-similarity,it has poor removal effect of sparse noise with structural information in the spectral dimension.Therefore,we propose a hyperspectral image denoising method based on superpixel block clustering and low-rank characteristics.The method realizes the adaptive partition and clustering of blocks,and makes full use of the non-local spatial self-similarity while retaining the local details.The experiments show that the same object block composed of clustered superpixel blocks has a good spatial-spectral dual low-rank attributes.Firstly,a superpixel segmentation method is applied to hyperspectral images,and the superpixel blocks are clustered to obtain the same object blocks.Secondly,the low-rank matrix restoration model is established and solved,and finally the denoised image is obtained.We conduct experiments on simulated data and real data respectively,and compare with other methods based on low-rank characteristics.The results show that this method has better denoising performance for mixed noise,especially sparse noise with structural information.

关 键 词：高光谱图像处理 降噪 低秩矩阵恢复 超像素分割 聚类 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]