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Trying to enhance the undertext in the SGP dataset

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sgp

Trying to enhance the undertext in the SGP dataset

Used Modules

python 3.7.4

  • network building:
    torch 1.4.0
  • data structures:
    pandas 0.25.1
    numpy 1.17.2
  • evaluation metrics:
    sklearn 0.23.2
  • images operation & curves drawing:
    skimage 0.16.2
    matlpotlib 3.1.1
    torchvision 0.5.0

To run & test:

  • networks/models.py: classes of all networks.
  • networks/xxx_classify.py: training of a network (xxx indicates the type of network)
  • networks/xxx_classify_test_roi.py: testing of a network, outputs enhancement reconstruction of a test image (NOTE: please run training before testing)
  • training data can be put under networks/data/sgp/xxx.csv (for pixel data) and networks/data/sgp/{folio_id}/cropped_roi/ (for cropped image patches)
  • intermediate folders created during training: networks/training_log/, networks/model/, networks/reconstructed_xxx/

Results preview (on cropped Tiff * rescale-0.25)

Images of 024r_029v
Original version, LDA version, AE-enhanced version, 1DConvNet, 2DFConvNet, 3DConvNet-hyb, Conv-hybrid:

Images of 102v_107r
Original version, LDA version, AE-enhanced version, 1DConvNet, 2DFConvNet, 3DConvNet-hyb, Conv-hybrid:

Images of 214v_221r
Original version, LDA version, AE-enhanced version, 1DConvNet, 2DFConvNet, 3DConvNet-hyb, Conv-hybrid:

Network Architecture

Stacked Autoencoder [1]

1DConvNet [2]

2DFConvNet

2DConvNet-hyb

Hybrid Convnet [3]

Reference

[1] C. Xing, L. Ma, and X. Yang. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images. Journal of Sensors, 2016:e3632943, 2015.

[2] Hu, W., Huang, Y., Wei, L., Zhang, F. and Li, H., 2015. Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015.

[3] Lee, H. and Kwon, H., 2016, July. Contextual deep CNN based hyperspectral classification. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3322-3325). IEEE.

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