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Satellite Imagery Feature Detection with SpaceNet dataset using deep UNet
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README.md

Deep UNet for satellite image segmentation

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About this project

This is a Keras based implementation of a deep UNet that performs satellite image segmentation.

Dataset

  • The dataset consists of 8-band commercial grade satellite imagery taken from SpaceNet dataset.
  • Train collection contains few tiff files for each of the 24 locations.
  • Every location has an 8-channel image containing spectral information of several wavelength channels (red, red edge, coastal, blue, green, yellow, near-IR1 and near-IR2). These files are located in data/mband/ directory.
  • Also available are correctly segmented images of each training location, called mask. These files contain information about 5 different classes: buildings, roads, trees, crops and water (note that original Kaggle contest had 10 classes).
  • Resolution for satellite images s 16-bit. However, mask-files are 8-bit.

Implementation

  • Deep Unet architecture is employed to perform segmentation.
  • Image augmentation is used for input images to significantly increases train data.
  • Image augmentation is also done while testing, mean results are exported to result.tif image. examples

Note: Training for this model was done on a Tesla P100-PCIE-16GB GPU.

Prediction Example

prediction example

Network architecture

Deep Unet Architecture

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