Gast ::
| Hauptseite > Publikationsdatenbank > The Influence of Sampling Methods on Pixel-Wise Hyperspectral Image Classification with 3D Convolutional Neural Networks |
| Hauptseite > Publikationsdatenbank > The Influence of Sampling Methods on Pixel-Wise Hyperspectral Image Classification with 3D Convolutional Neural Networks |
| Contribution to a conference proceedings/Contribution to a book | FZJ-2018-04967 |
; ; ; ;
2018
This record in other databases:
Please use a persistent id in citations: http://hdl.handle.net/2128/19910 doi:10.1109/IGARSS.2018.8518671
Abstract: Supervised image classification is one of the essential techniques for generating semantic maps from remotely sensed images.The lack of labeled ground truth datasets, due to the inherent time effort and cost involved in collecting training samples, has led to the practice of training and validating new classifiers within a single image. In line with that, the dominant approach for the division of the available ground truth into disjoint training and test sets is random sampling. This paper discusses the problems that arise when this strategy is adopted in conjunction with spectral-spatial and pixel-wise classifiers such as 3D Convolutional Neural Networks (3D CNN). It is shown that a random sampling scheme leads to a violation of the independence assumption and to the illusion that global knowledge is extracted from the training set.To tackle this issue, two improved sampling strategies based on the Density-Based Clustering Algorithm (DBSCAN) are proposed. They minimize the violation of the train and test samples independence assumption and thus ensure an honest estimation of the generalization capabilities of the classifier.
|
The record appears in these collections: |
PDF
PDF (PDFA)