li123 / deep-unet-for-satellite-image-segmentation

Satellite Imagery Feature Detection with SpaceNet dataset using deep UNet

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.

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

Prediction Example

Network architecture