AnujTiwari / Label-Pixels

Label-Pixels is a tool for semantic segmentation of remote sensing images using fully convolutional networks (FCNs), designed for extracting the road network from remote sensing imagery and it can be used in other applications applications to label every pixel in the image ( Semantic segmentation).

Label-Pixels

Label-Pixels is a tool for semantic segmentation of remote sensing images using fully convolutional networks (FCNs), designed for extracting the road network from remote sensing imagery and it can be used in other applications applications to label every pixel in the image ( Semantic segmentation). This is part of my MSc research project (Automatic Road Extraction from High-Resolution Remote Sensing Imagery using Fully Convolutional Networks and Transfer Learning).

Clone repository and install packages in Anaconda

git clone https://github.com/venkanna37/Label-Pixels.git

conda env create -f environment.yml

OR

conda install -c conda-forge keras
conda install -c conda-forge gdal
conda install -c anaconda scikit-learn

Usage

Rasterize

• Creates labels with shapefiles
• The projection of imagery and shapefiles should be same
• Projection units should be in meters if you want to buffer line feature

options	Description
--help	Print usage information
--raster	Raster/Image name with directory
--vector	Vector file name with directory
--output_file	Output filename with directory
--buffer	Buffer length for line feature. Not required for polygon
--buffer_atr	Attribute from the vector file, this attribute can be buffer width and It multiplies with --buffer. Not required for polygon
--labels_atr	Attribute from the vector file, pixels inside the polygon will be assigned by its attribute value. Not required for line

Example:

python rasterize.py --raster ../data/spacenet/raster/spacenet_chip0.tif --vector ../data/spacenet/vector/spacenet_chip0.shp --buffer 2 --buffer_atr lanes --output_file ../data/spacenet/binary/test.tif

Patch Generation

• Generate patches from Images/Tiles
• To generate patches for train, test and valid sets, the command needs to be run three times
• Name of image and label files should be same

options	Description
--image_folder	Folder of input images/tiles with directory
--image_format	Image format tiff/tif/jpg/png
--label_folder	Folder of label images with directory
--label_format	Label format tiff/tif/jpg/png
--patch_size	Patch size to feed network. Default size is 256
--overlap	Overlap between two patches on image/tile
--output_folder	Output folder to save patches

Example:

python patch_gen.py --image_folder ../data/mass_sample/test/image/ --image_format tiff --label_folder ../data/mass_sample/test/roads_and_buildings/ --label_format tif --patch_size 256 --output_folder ../data/mass_patches/

CSV Paths

• Save directories of patches in CSV file instead of reading patches from folders directly

options	Description
--image_folder	Folder of image patches with directory
--image_format	Image format tif (patch_gen.py save patches in tif format)
--label_folder	Folder of label patches with directory
--label_format	Label format tif (patch_gen.py save patches in tif format)
--patch_size	Patch size to feed network. Default size is 256
--output_csv	csv filename with directory

Example

python csv_paths.py --image_folder ../data/mass_patches/image/ --image_format tif --label_folder ../data/mass_patches/label/ --label_format tif --output_csv ../paths/data_rd.csv

Training

• Training FCNs for semantic segmentation

options	Description
--model	Name of FCN model. Existing models are unet, segnet and resunet
--train_csv	CSV file name with directory, consists of directories of image and label patches of training set.
--valid_csv	CSV file name with directory, consists of directories of image and label patches of validation set.
--input_shape	Input shape of model to feed (patch_size patch_size channels)
--batch_size	Batch size, depends on GPU/CPU memory
--num_classes	Number of classes in labels data
--epochs	Number of epochs

Example

python train.py --model unet --train_csv ../paths/data_rd.csv --valid_csv ../paths/data_rd.csv --input_shape 256 256 3 --batch_size 1 --num_classes 3 --epochs 100

Accuracy

• Calculates the accuracy using different accuracy metrics.

options	Description
--input_shape	Input shape of model (patch_size patch_size channels)
--weights	Trained model with directory
--csv_paths	CSV file name with directory, consists of directories of image and label patches of test set.
--num_classes	Number of classes in labels data

Example

python accuracy.py --model unet --input_shape 256 256 3 --weights ../trained_models/unet300_06_07_20.hdf5 --csv_paths ../paths/data_rd.csv --num_classes 3

Prediction

• Predicts the the entire image/tile with trained model.

options	Description
--input_shape	Input shape of model (patch_size patch_size channels)
--weights	Trained model with directory
--image_folder	Folder of input images/tiles with directory
--image_format	Image format tiff/tif/jpg/png
--output_folder	Output folder to save predicted images/tiles

Example:

python tile_predict.py --model unet --input_shape 256 256 3 --weights ../trained_models/unet300_06_07_20.hdf5 --image_folder ../data/mass_sample/test/image/ --image_format tiff --output_folder ../data/

Summary of the Model

• Summary of FCNs

options	Description
--model	Name of FCN model. Existing models are unet, segnet and resunet
--input_shape	Input shape of model to feed (patch_size patch_size channels)
--num_classes	Number of classes to train

Example

python summary.py --model unet --input_shape 256 256 3 --num_classes 3

Example Outputs

Benchmark datasets

1. Massachusetts Benchmark datasets for Roads and Buildings extraction
   https://academictorrents.com/browse.php?search=Volodymyr+Mnih
2. List of Benchmark datasets for semantic segmentation, object detection from remote sensing imagery https://github.com/chrieke/awesome-satellite-imagery-datasets

Any problem with code?

Open issue or mail me 👉 [e-mail]