Tao Hu , Zijie Chen , Mingyi Wang , Xintong Hou , Xiaoping Lu , Yuanyuan Pan , Jianqing Li
{"title":"用于遥感图像超分辨率的全局稀疏注意力网络","authors":"Tao Hu , Zijie Chen , Mingyi Wang , Xintong Hou , Xiaoping Lu , Yuanyuan Pan , Jianqing Li","doi":"10.1016/j.knosys.2024.112448","DOIUrl":null,"url":null,"abstract":"<div><p>Recently, remote sensing images have become popular in various tasks, including resource exploration. However, limited by hardware conditions and formation processes, the obtained remote sensing images often suffer from low-resolution problems. Unlike the high cost of hardware to acquire high-resolution images, super-resolution software methods are good alternatives for restoring low-resolution images. In addition, remote sensing images have a common nature that similar visual patterns repeatedly appear across distant locations. To fully capture these long-range satellite image contexts, we first introduce the global attention network super-resolution method to reconstruct the images. This network improves the performance but introduces unessential information while significantly increasing the computational effort. To address these problems, we propose an innovative method named the global sparse attention network (GSAN) that integrates both sparsity constraints and global attention. Specifically, our method applies spherical locality sensitive hashing (SLSH) to convert feature elements into hash codes, constructs attention groups based on the hash codes, and computes the attention matrix according to similar elements in the attention group. Our method captures valid and useful global information and reduces the computational effort from quadratic to asymptotically linear regarding the spatial size. Extensive qualitative and quantitative experiments demonstrate that our GSAN has significant competitive advantages in terms of performance and computational cost compared with other state-of-the-art methods.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Global sparse attention network for remote sensing image super-resolution\",\"authors\":\"Tao Hu , Zijie Chen , Mingyi Wang , Xintong Hou , Xiaoping Lu , Yuanyuan Pan , Jianqing Li\",\"doi\":\"10.1016/j.knosys.2024.112448\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Recently, remote sensing images have become popular in various tasks, including resource exploration. However, limited by hardware conditions and formation processes, the obtained remote sensing images often suffer from low-resolution problems. Unlike the high cost of hardware to acquire high-resolution images, super-resolution software methods are good alternatives for restoring low-resolution images. In addition, remote sensing images have a common nature that similar visual patterns repeatedly appear across distant locations. To fully capture these long-range satellite image contexts, we first introduce the global attention network super-resolution method to reconstruct the images. This network improves the performance but introduces unessential information while significantly increasing the computational effort. To address these problems, we propose an innovative method named the global sparse attention network (GSAN) that integrates both sparsity constraints and global attention. Specifically, our method applies spherical locality sensitive hashing (SLSH) to convert feature elements into hash codes, constructs attention groups based on the hash codes, and computes the attention matrix according to similar elements in the attention group. Our method captures valid and useful global information and reduces the computational effort from quadratic to asymptotically linear regarding the spatial size. Extensive qualitative and quantitative experiments demonstrate that our GSAN has significant competitive advantages in terms of performance and computational cost compared with other state-of-the-art methods.</p></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124010827\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124010827","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Global sparse attention network for remote sensing image super-resolution
Recently, remote sensing images have become popular in various tasks, including resource exploration. However, limited by hardware conditions and formation processes, the obtained remote sensing images often suffer from low-resolution problems. Unlike the high cost of hardware to acquire high-resolution images, super-resolution software methods are good alternatives for restoring low-resolution images. In addition, remote sensing images have a common nature that similar visual patterns repeatedly appear across distant locations. To fully capture these long-range satellite image contexts, we first introduce the global attention network super-resolution method to reconstruct the images. This network improves the performance but introduces unessential information while significantly increasing the computational effort. To address these problems, we propose an innovative method named the global sparse attention network (GSAN) that integrates both sparsity constraints and global attention. Specifically, our method applies spherical locality sensitive hashing (SLSH) to convert feature elements into hash codes, constructs attention groups based on the hash codes, and computes the attention matrix according to similar elements in the attention group. Our method captures valid and useful global information and reduces the computational effort from quadratic to asymptotically linear regarding the spatial size. Extensive qualitative and quantitative experiments demonstrate that our GSAN has significant competitive advantages in terms of performance and computational cost compared with other state-of-the-art methods.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.