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A hybrid OSVM OCNN Method for Crop Classification from Fine Spatial Resolution Remotely Sensed Imagery

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Figure

Figure 1. Flowchart illustrating the presented object-based support vector machine-object-based convolutional neural network (OSVM-OCNN) method with four major steps: (A) image segmentation, (B) model training, (C) model inference and (D) fusion decision
Figure 2. Two examples to illustrate the convolutional position (green star) of a specific object (highlighted cyan polygon) as well as the corresponding convolutional input window (yellow rectangle); the other segmented patches are delineated by red polyg
Figure 3. (a) A subset of the UAVSAR image (bands VV, HV and HH) used in this paper, (b) the prediction probability generated by the OSVM model, (c) the prediction probability achieved by the OCNN model
Figure 4. The two study sites S1 and S2 over the agricultural district of the Sacramento Valley, California
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