FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS)

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Deep Learning FactSeg
Overview

FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery

by Ailong Ma, Junjue Wang*, Yanfei Zhong* and Zhuo Zheng




This is an official implementation of FactSeg in our TGRS paper " FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery "

Citation

If you use FactSeg in your research, please cite our coming TGRS paper.

@ARTICLE{FactSeg,
  author={Ma Ailong, Wang Junjue, Zhong Yanfei and Zheng Zhuo},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery}, 
  year={2021},
  volume={},
  number={},
  pages={1-16},
  doi={10.1109/TGRS.2021.3097148}}

This is follow-up work of our FarSeg (CVPR2020).

@inproceedings{zheng2020foreground,
  title={Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4096--4105},
  year={2020}
}

Getting Started

Install SimpleCV

pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.git

Requirements:

  • pytorch >= 1.1.0
  • python >=3.6

Prepare iSAID Dataset

ln -s </path/to/iSAID> ./isaid_segm

Evaluate Model

1. download pretrained weight in Google Drive

2. move weight file to log directory

mkdir -vp ./log/
mv ./factseg50.pth ./log/model-60000.pth

3. inference on iSAID val

bash ./scripts/eval_factseg.sh

Train Model

bash ./scripts/train_factseg.sh
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Comments
  • Discrepancy in calculation of {B1, B2, B3, B4} in manuscript

    Discrepancy in calculation of {B1, B2, B3, B4} in manuscript

    In the manuscript, the calculation of B1, B2, B3 and B4 is given by

    Bi+1 = Upsamplex2(T(Bi)) + D(F4-i)

    Which means for calculating B2, we need to upsample B1 then elementwise add the transformed feature map F3 followed by a 3x3 convolution.

    But in the implementation, this is not how B2 is calculated, nor B3 and neither B4.

    The implementation basically upsamples F4 and then elementwise adds the transformed feature map F3 on which a 3x3 convolution is performed to obtain B2. There is no upsampling of B1.

    Is this discrepancy an inadvertant mistake or am I missing something?

    PS. According to the original FPN paper, the later is the correct procedure to calculate the feature pyramids.

    opened by manupillai308 5
Owner
Kingdrone
Deep learning in RS
Kingdrone
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