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Machine Learning Algorithms for Spatio-Temporal Scaling of Remotely Sensed Data
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Chakrabarti, Subit. |
| 단체저자명 | University of Florida. Electrical and Computer Engineering. |
| 서명/저자사항 | Machine Learning Algorithms for Spatio-Temporal Scaling of Remotely Sensed Data. |
| 발행사항 | [S.l.] : University of Florida., 2017 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2017 |
| 형태사항 | 181 p. |
| 소장본 주기 | School code: 0070. |
| ISBN | 9780438165601 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
|
| 요약 | Over the last decade, machine learning algorithms have been extensively studied for their ability to estimate non-linear and dynamic mappings among sets of variables with high accuracies, even for large datasets. With satellite sensors providing |
| 요약 | This dissertation develops novel machine learning algorithms to downscale RS observations of soil moisture (SM) and microwave brightness temperatures (TB) utilizing spatial and temporal correlations among other disparate RS and in-situ datasets. |
| 요약 | The algorithms use kernel methods, information theoretic learning and ensemble learning to provide estimates of high resolution SM and TB satisfying different conditions of variable complexities, data availabilities and expected computational pe |
| 일반주제명 | Electrical engineering. Hydrologic sciences. Remote sensing. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T15000763 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00025677 | 621.3 | 가야대학교// | 대출가능 |
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