Keywords
Hyperspectral imagingRandom forestLightning (connector)Remote sensingSupport vector machineForest healthForestryEnvironmental scienceGeographyArtificial intelligenceComputer science
Affiliated Institutions
Related Publications
Implementation of machine-learning classification in remote sensing: an applied review
Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle dat...
Classification and Regression by randomForest
Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, ...
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is q...
Linear spectral mixture models and support vector machines for remote sensing
Mixture modeling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve subpixel, area information. This paper compares a w...
Local Features and Kernels for Classification of Texture and Object Categories: An In-Depth Study
Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that rep...
Publication Info
- Year
- 2013
- Type
- article
- Volume
- 88
- Pages
- 48-59
- Citations
- 164
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
164
OpenAlex
Cite This
Elfatih M. Abdel‐Rahman,
Onisimo Mutanga,
Elhadi Adam
et al.
(2013).
Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers.
ISPRS Journal of Photogrammetry and Remote Sensing
, 88
, 48-59.
https://doi.org/10.1016/j.isprsjprs.2013.11.013
Identifiers
- DOI
- 10.1016/j.isprsjprs.2013.11.013