{"title":"基于地图算法的Terra MODIS 5波段条纹噪声检测与校正","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":"{\"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}","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
摘要
由于Terra MODIS波段5(1.230 ~ 1.250µm)的20个探测器中有1个存在噪声,因此在整个图像上存在尖锐且重复的条纹。对于MODIS定位数据来说,条纹是不规则的,有时甚至是不连续的,这给图像检索带来了难题。本文提出了一种提取条纹噪声的检测方法,并基于最大后验(MAP)算法对污染像素进行校正。在MAP方法中,基于线性图像噪声模型提出了似然概率密度函数,并采用Huber-Markov模型作为先验似然概率密度函数。采用梯度下降优化方法接收去条纹图像。该算法已在Terra MODIS 5波段定位图像上进行了测试。实验结果表明,该算法具有良好的性能。
Terra MODIS band 5 Stripe noise detection and correction using MAP-based algorithm
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.