상세정보
Export to Refworks부가기능
Computational Techniques to Identify Rare Events in Spatio-Temporal Data
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Computational Techniques to Identify Rare Events in Spatio-Temporal Data. |
| 개인저자 | Mithal, Varun. |
| 단체저자명 | University of Minnesota. Computer Science. |
| 발행사항 | [S.l.]: University of Minnesota., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 109 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438168640 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Vipin Kumar. |
| 요약 | Recent attention on the potential impacts of land cover changes to the environment as well as long-term climate change has increased the focus on automated tools for global-scale land surface monitoring. Advancements in remote sensing and data c |
| 요약 | We study the problem of identifying land cover changes such as forest fires as a supervised binary classification task with the following characteristics: (i) instead of true labels only imperfect labels are available for training samples. These |
| 요약 | We explore approaches to reduce errors in remote sensing based classification products, which are common due to poor data quality (eg., instrument failure, atmospheric interference) as well as limitations of the classification models. We present |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
