{"title":"同质与异质环境时空变化的遥感数据集","authors":"Thaer F. Ali, A. Woodley","doi":"10.1109/DICTA47822.2019.8946005","DOIUrl":null,"url":null,"abstract":"Standard experimental datasets permit comprehensive analysis between approaches. These datasets are ubiquitous in many data science domains but uncommon in remote sensing. This paper presents the Spatial-Temporal Change in Environmental Context (STCEC) dataset, an experimental remote sensing dataset that contains changes (and non-changes) in homogeneous and heterogeneous environments, thereby, enabling researchers to test their approaches in different contexts. STCEC was tested with five pixel interpolation approaches and showed a significant difference between changes in homogeneous and heterogeneous environments. It is hoped that the dataset will be used by other researchers in future work.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"36 1","pages":"1-8"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"STCEC: A Remote Sensing Dataset for Identifying Spatial-Temporal Change in Homogeneous and Heterogeneous Environments\",\"authors\":\"Thaer F. Ali, A. Woodley\",\"doi\":\"10.1109/DICTA47822.2019.8946005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Standard experimental datasets permit comprehensive analysis between approaches. These datasets are ubiquitous in many data science domains but uncommon in remote sensing. This paper presents the Spatial-Temporal Change in Environmental Context (STCEC) dataset, an experimental remote sensing dataset that contains changes (and non-changes) in homogeneous and heterogeneous environments, thereby, enabling researchers to test their approaches in different contexts. STCEC was tested with five pixel interpolation approaches and showed a significant difference between changes in homogeneous and heterogeneous environments. It is hoped that the dataset will be used by other researchers in future work.\",\"PeriodicalId\":6696,\"journal\":{\"name\":\"2019 Digital Image Computing: Techniques and Applications (DICTA)\",\"volume\":\"36 1\",\"pages\":\"1-8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Digital Image Computing: Techniques and Applications (DICTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DICTA47822.2019.8946005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Digital Image Computing: Techniques and Applications (DICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DICTA47822.2019.8946005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
STCEC: A Remote Sensing Dataset for Identifying Spatial-Temporal Change in Homogeneous and Heterogeneous Environments
Standard experimental datasets permit comprehensive analysis between approaches. These datasets are ubiquitous in many data science domains but uncommon in remote sensing. This paper presents the Spatial-Temporal Change in Environmental Context (STCEC) dataset, an experimental remote sensing dataset that contains changes (and non-changes) in homogeneous and heterogeneous environments, thereby, enabling researchers to test their approaches in different contexts. STCEC was tested with five pixel interpolation approaches and showed a significant difference between changes in homogeneous and heterogeneous environments. It is hoped that the dataset will be used by other researchers in future work.