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Classification maps of different CNN methods for the training area: (a) 2D CNN, (b) 3D CNN, and (c) HybridSN.

Classification maps of different CNN methods for the training area: (a) 2D CNN, (b) 3D CNN, and (c) HybridSN.

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Figure 3. An illustration of the CNN architecture used in this study....
Figure 6. Classification maps of different CNN methods for the training...
Figure 7. Classification maps of different CNN methods for the test area.
Figure 9. Convergence plots showing the progress of Bayesian...
+7 Figure 10. Classification maps of different methods used in this...
A Joint Bayesian Optimization for the Classification of Fine Spatial Resolution Remotely Sensed Imagery Using Object-Based Convolutional Neural Networks
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  • Oct 2022
In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped image patches, they cannot accurately extract objects’ boundaries, especially in complex urban sett...
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... proposed classification workflow experimented with three CNN models, namely, 2D CNN, 3D CNN, and HybridSN. A visual comparison of these methods as classification maps is presented in Figures 6 and 7 for the training and test areas, respectively. Table 4 presents the classification accuracies obtained for the three methods in the training and test areas including the overall accuracies (OA, Kappa, mIoU) as well as perclass accuracies. ...