Remote Sensing | |
Determining Subcanopy |
|
Jomar M. Barbosa3  Gregory P. Asner3  Roberta E. Martin3  Claire A. Baldeck3  Flint Hughes1  Tracy Johnson1  Susan L. Ustin2  Parth Sarathi Roy2  | |
[1] Institute of Pacific Islands Forestry, Pacific Southwest Research Station, USDA Forest Service, Hilo, HI 96720, USA;;Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USADepartment of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA; | |
关键词: invasive species; strawberry guava; single-class classification; mixture tuned matched filtering; biased support vector machine; Carnegie Airborne Observatory; | |
DOI : 10.3390/rs8010033 | |
来源: mdpi | |
【 摘 要 】
High-resolution airborne imaging spectroscopy represents a promising avenue for mapping the spread of invasive tree species through native forests, but for this technology to be useful to forest managers there are two main technical challenges that must be addressed: (1) mapping a single focal species amongst a diverse array of other tree species; and (2) detecting early outbreaks of invasive plant species that are often hidden beneath the forest canopy. To address these challenges, we investigated the performance of two single-class classification frameworks—Biased Support Vector Machine (BSVM) and Mixture Tuned Matched Filtering (MTMF)—to estimate the degree of
【 授权许可】
CC BY
© 2016 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202003190000394ZK.pdf | 6333KB | download |