A tree crown segmentation model using a trainable Log-Gabor convolutional layer, mixed pooling, and averaged dilated convolutions reports mIoU gains over ResUNet and other baselines on three aerial datasets.
U-net: Con- volutional networks for biomedical image segmentation,
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Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation
A tree crown segmentation model using a trainable Log-Gabor convolutional layer, mixed pooling, and averaged dilated convolutions reports mIoU gains over ResUNet and other baselines on three aerial datasets.