{"title":"Terra MODIS band 5 Stripe noise detection and correction using MAP-based algorithm","authors":"Rongbin Wang, Chao Zeng, Pingxiang Li, Huanfeng Shen","doi":"10.1109/RSETE.2011.5964181","DOIUrl":null,"url":null,"abstract":"Since 1 of the 20 detectors in Terra MODIS band 5 (1.230∼1.250 µm) are noisy, there are sharp and repetitive stripes over the entire image. As for MODIS geolocated data, the stripes are irregular and sometimes uncontinuous, it brings a difficult problem to the image retrieving process. This paper presents a detection method to extract the stripe noise, and a maximum a posteriori (MAP) based algorithm to correct the contaminated pixels. In the MAP method, the likelihood probability density function (PDF) is proposed based on a linear image noise model, and a Huber-Markov model is employed as the prior PDF. The gradient descent optimization method is used to receive the destriped image. The proposed algorithm has been tested using a Terra MODIS band 5 geolocated image. The experimental results demonstrate that the proposed algorithm performs well.","PeriodicalId":6296,"journal":{"name":"2011 International Conference on Remote Sensing, Environment and Transportation Engineering","volume":"17 1","pages":"8612-8615"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Remote Sensing, Environment and Transportation Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RSETE.2011.5964181","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
Since 1 of the 20 detectors in Terra MODIS band 5 (1.230∼1.250 µm) are noisy, there are sharp and repetitive stripes over the entire image. As for MODIS geolocated data, the stripes are irregular and sometimes uncontinuous, it brings a difficult problem to the image retrieving process. This paper presents a detection method to extract the stripe noise, and a maximum a posteriori (MAP) based algorithm to correct the contaminated pixels. In the MAP method, the likelihood probability density function (PDF) is proposed based on a linear image noise model, and a Huber-Markov model is employed as the prior PDF. The gradient descent optimization method is used to receive the destriped image. The proposed algorithm has been tested using a Terra MODIS band 5 geolocated image. The experimental results demonstrate that the proposed algorithm performs well.