Jan 6, 2021 · We introduce O-SegNet- the robust encoder and decoder architecture for objects segmentation from high-resolution aerial imagery data to address ...
The segmentation of diversified roads and buildings from high-resolution aerial images is essential for various applications, such as urban planning, ...
We introduce O-SegNet- the robust encoder and decoder architecture for objects segmentation from high-resolution aerial imagery data to address this challenge.
The segmentation of diversified roads and buildings from high-resolution aerial images is essential for various applications, such as urban planning, ...
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O-SegNet: Robust encoder and decoder architecture for objects segmentation from aerial imagery data. KK Eerapu, S Lal, AV Narasimhadhan. IEEE Transactions on ...
Apr 25, 2024 · O-SegNet: Robust Encoder and Decoder Architecture for Objects Segmentation From Aerial Imagery Data. IEEE Trans. Emerg. Top. Comput. Intell ...
We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet.
This paper chooses UNet as the baseline model, using a coordinate attention-based residual network in the encoder to improve the extraction capability of ...
Eerapu, O-segnet: robust encoder and decoder architecture for objects segmentation from aerial imagery data, IEEE Trans. ... strong encoders for medical image ...
Semantic segmentation is one of most the important computer vision tasks for the analysis of aerial imagery in many remote.