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Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

https://doi.org/10.3390/app9091816
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Outgoing claims: the papers this work cites, builds on, or argues with.

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SeNet: Structured Edge Network for Sea–Land Segmentation
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Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
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CoinNet: Copy Initialization Network for Multispectral Imagery Semantic Segmentation
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Robust Autodual Morphological Profiles for the Classification of High-Resolution Satellite Images
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Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification
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Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks
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inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Peer Review #2 of "A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans (v0.1)"
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Peer Review #1 of "A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans (v0.2)"
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Peer Review #2 of "A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans (v0.2)"
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A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans
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