{"title":"遥感数据分析的人工智能:挑战与机遇综述","authors":"Lefei Zhang, Liangpei Zhang","doi":"10.1109/mgrs.2022.3145854","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"270-294"},"PeriodicalIF":16.2000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"116","resultStr":"{\"title\":\"Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities\",\"authors\":\"Lefei Zhang, Liangpei Zhang\",\"doi\":\"10.1109/mgrs.2022.3145854\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research.\",\"PeriodicalId\":48660,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Magazine\",\"volume\":\"10 1\",\"pages\":\"270-294\"},\"PeriodicalIF\":16.2000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"116\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Magazine\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1109/mgrs.2022.3145854\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Magazine","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1109/mgrs.2022.3145854","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research.
期刊介绍:
The IEEE Geoscience and Remote Sensing Magazine (GRSM) serves as an informative platform, keeping readers abreast of activities within the IEEE GRS Society, its technical committees, and chapters. In addition to updating readers on society-related news, GRSM plays a crucial role in educating and informing its audience through various channels. These include:Technical Papers,International Remote Sensing Activities,Contributions on Education Activities,Industrial and University Profiles,Conference News,Book Reviews,Calendar of Important Events.