Aozhe Dou, Yang Hao, Weifeng Liu, Liangliang Li, Zhenzhong Wang, Baodi Liu
{"title":"基于多尺度空间信息感知的遥感图像云去除技术","authors":"Aozhe Dou, Yang Hao, Weifeng Liu, Liangliang Li, Zhenzhong Wang, Baodi Liu","doi":"10.1007/s00530-024-01442-5","DOIUrl":null,"url":null,"abstract":"<p>Remote sensing imagery is indispensable in diverse domains, including geographic information systems, climate monitoring, agricultural planning, and disaster management. Nonetheless, cloud cover can drastically degrade the utility and quality of these images. Current deep learning-based cloud removal methods rely on convolutional neural networks to extract features at the same scale, which can overlook detailed and global information, resulting in suboptimal cloud removal performance. To overcome these challenges, we develop a method for cloud removal that leverages multi-scale spatial information perception. Our technique employs convolution kernels of various sizes, enabling the integration of both global semantic information and local detail information. An attention mechanism enhances this process by targeting key areas within the images, and dynamically adjusting channel weights to improve feature reconstruction. We compared our method with current popular cloud removal methods across three datasets, and the results show that our proposed method improves metrics such as PSNR, SSIM, and cosine similarity, verifying the effectiveness of our method in cloud removal.</p>","PeriodicalId":51138,"journal":{"name":"Multimedia Systems","volume":"11 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remote sensing image cloud removal based on multi-scale spatial information perception\",\"authors\":\"Aozhe Dou, Yang Hao, Weifeng Liu, Liangliang Li, Zhenzhong Wang, Baodi Liu\",\"doi\":\"10.1007/s00530-024-01442-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Remote sensing imagery is indispensable in diverse domains, including geographic information systems, climate monitoring, agricultural planning, and disaster management. Nonetheless, cloud cover can drastically degrade the utility and quality of these images. Current deep learning-based cloud removal methods rely on convolutional neural networks to extract features at the same scale, which can overlook detailed and global information, resulting in suboptimal cloud removal performance. To overcome these challenges, we develop a method for cloud removal that leverages multi-scale spatial information perception. Our technique employs convolution kernels of various sizes, enabling the integration of both global semantic information and local detail information. An attention mechanism enhances this process by targeting key areas within the images, and dynamically adjusting channel weights to improve feature reconstruction. We compared our method with current popular cloud removal methods across three datasets, and the results show that our proposed method improves metrics such as PSNR, SSIM, and cosine similarity, verifying the effectiveness of our method in cloud removal.</p>\",\"PeriodicalId\":51138,\"journal\":{\"name\":\"Multimedia Systems\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multimedia Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s00530-024-01442-5\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multimedia Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s00530-024-01442-5","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Remote sensing image cloud removal based on multi-scale spatial information perception
Remote sensing imagery is indispensable in diverse domains, including geographic information systems, climate monitoring, agricultural planning, and disaster management. Nonetheless, cloud cover can drastically degrade the utility and quality of these images. Current deep learning-based cloud removal methods rely on convolutional neural networks to extract features at the same scale, which can overlook detailed and global information, resulting in suboptimal cloud removal performance. To overcome these challenges, we develop a method for cloud removal that leverages multi-scale spatial information perception. Our technique employs convolution kernels of various sizes, enabling the integration of both global semantic information and local detail information. An attention mechanism enhances this process by targeting key areas within the images, and dynamically adjusting channel weights to improve feature reconstruction. We compared our method with current popular cloud removal methods across three datasets, and the results show that our proposed method improves metrics such as PSNR, SSIM, and cosine similarity, verifying the effectiveness of our method in cloud removal.
期刊介绍:
This journal details innovative research ideas, emerging technologies, state-of-the-art methods and tools in all aspects of multimedia computing, communication, storage, and applications. It features theoretical, experimental, and survey articles.