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- Title
IMPROVING THE BINARY CLASSIFICATION OF PEAT LOCALITIES FROM MULTI-SOURCE REMOTELY-SENSED DATA USING CNN.
- Authors
Pittman, R.; Hu, B.
- Abstract
Neural networks were explored to achieve a binary classification for determining land corresponding to peat for a study area in the boreal forest of northern Ontario, Canada. Environmental covariates were employed as predictors and obtained from multiple sources, which included multispectral imagery, LiDAR, SAR, and aeromagnetic data. A dense neural network (DNN), as well as a convolutional neural network (CNN), were each implemented. Logistic regression, support vector machine (SVM) and random forest (RF) approaches were also modelled. Neighboring pixels surrounding the soil sampling sites were incorporated as input into the CNN, that permitted training on additional information that was not exploited by other methods. Preliminary results indicate that a CNN can attain improved accuracies for peat classification, when compared against other approaches.
- Subjects
ONTARIO; PEAT; CONVOLUTIONAL neural networks; LOGISTIC regression analysis; SUPPORT vector machines; TAIGAS; RANDOM forest algorithms; LAND cover
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2022, Issue B3, p983
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLIII-B3-2022-983-2022